Prompt:
Job Description:
financial-advisor

Candidate A Resume:
{"data": {"awards": [], "certifications": [], "education": [{"GPA": "", "degree": "Bachelor of Technology", "endYear": "2014", "major": "Mechanical Engineering", "school": "IIT Bombay", "startYear": "2010"}, {"GPA": "", "degree": "Master of Business Administration", "endYear": "2019", "major": "Finance & Economics", "school": "IIM Calcutta", "startYear": "2017"}], "projects": [], "publication": [], "skills": ["Python", "VBA", "Risk Management", "Trading (FICC)", "Hindi", "English", "Gujrati"], "workExperience": [{"company": "CoinDcx (Neblio Technologies Pvt. Ltd.)", "description": "Set up the OTC desk for crypto market & Quant team for Market Making (MM) for exchange. Lead and managed the recruitment of 7-person team, Operation, strategy, P&L management and product requirement and development for the desk. Trained the team in trading, research and strategy building aspect, creating internal research reports for the exchange from the markets POV. Scaled the OTC desk to one of the largest in country for BTC, USDT & ETH, became the first desk to transact for a non-crypto firm for treasury account in India. Built the MM strategy flow and logic deploying the internal capital to manage Order books (OB) covering 300+ pairs in INR. Initiated the strategies for alpha generation exploiting Momentum, arbitrage, derivatives model (gamma, volatility surface etc.), OB imbalances & indicators. Built the hedging strategy and initiated proprietary trading (both directional and RV) for OTC desk to manage risk and generate P&L for the desk. Consistently positive contribution to firm's bottom line across the three years (~20% of firm's bottom line in FY 2023).", "endYear": "2024", "location": {"city": "Bengaluru", "country": "India"}, "role": "Director-OTC desk and Quant", "startYear": "2021"}, {"company": "Consultant (Freelance) - OTC desk and Quant", "description": "Advising the organization on P&L management for Quantitative strategy and OTC desk, order book for derivatives (futures and options) and Dubai OTC desk set up. Consultation for scaling up & development of trading strategies and development of tools to manage the strategies.", "endYear": "Present", "location": {"city": "", "country": ""}, "role": "Consultant (Freelance) - OTC desk and Quant", "startYear": "2024"}, {"company": "BristleStar Research & Consulting", "description": "Research on U.S. and European FI and STIR derivatives (Bond futures, SOFR, Fed Funds & Euribor futures) to create strategies for proprietary trading: Quantitative pricing models and strategy on outright, spreads and fly for the derivatives market (yield curve & STIR curve for U.S. and Europe). Macroeconomic and events research, analysis and trading ideas predominantly for FI along with covering equity indices, currency and commodity derivative.", "endYear": "Present", "location": {"city": "", "country": ""}, "role": "Consultant (Freelance, remote) - Fixed Income derivatives", "startYear": "2021"}, {"company": "J. P. Morgan & Chase", "description": "Implemented model for pricing long dated illiquid options in Emerging Market for the interest rates product. Developed a tool for monitoring and pricing yield curve spread option using Gaussian Copula. Worked on model enhancement for Bank owned Life Insurance wrap to improve the pricing methodology. Developed framework to model forward yields for Bloomberg/Barclays Fixed rate Agency MBS index covering equity indices, currency and commodity derivative.", "endYear": "2020", "location": {"city": "Dublin", "country": "Ireland"}, "role": "Associate - Quantitative Research", "startYear": "2019"}, {"company": "Axxela (formerly Halftick) Pvt. Ltd.", "description": "Managed & traded positions in U.S.A., U.K. & German F.I. futures (Short Sterling, Gilts, Euribor, Eurodollar, German Bund, Bobl & Schatz, Treasury Notes futures) on ICE, Eurex CBOT & CME. Traded Outrights, Spreads, Flies, Boxes, TEDs, Yield curve spreads; net profit of $130K+ (highest in fresh peers). Created 12+ new trading strategies; increased the firm's volume traded by 15%; traded 600k lots for 2016-17; Optimized risk adjusted return to take positions prior and after Brexit and made highest single day profit of $25K. Identified preopening arbitrage opportunities in short sterling market leading to $4.5k increase in weekly profit; Created model to calculate DV01 for bonds which led to Improved hedge ratios. Conducted technical analysis using indicators MACD, RSI, Bollinger Bands; Devised queuing method exploiting implied functionality of exchanges leading to 30% increase in Firm's volume. Recognitions and Initiatives: Amongst the top 3 traders in the firm with regards to capital deployed, clip size, net profit and volume. Led the team of 3 to initiate algorithmic trading strategies for replacement of mechanical execution of trading; Trained, supervised & managed 3 new traders in various aspects of fixed income markets & derivatives trading.", "endYear": "Present", "location": {"city": "", "country": ""}, "role": "Derivatives Trader: Fixed Income", "startYear": "Present"}, {"company": "Goldman Sachs (GPS: Multi Assets Management)", "description": "Analysis to identify relative performance regime between equity market in Latin America and Asia Pacific excl. Japan using statistical and empirical factors (inflation, GDP, Chinese growth, P/B etc.). Identified effectiveness of equity sector's own momentum in timing them over different time period windows. Analyzed the contribution of sectoral differences to value vs growth and size styles in U.S.A. equity market. Analyzed metrics to time equity factors & approach's efficacy; framework to time low vol factor vs momentum. Identified characteristics of different kind of Money Markets funds and impact on performance due to rate rise.", "endYear": "2017", "location": {"city": "Mumbai", "country": "India"}, "role": "Summer Intern: Investment Center", "startYear": "2015"}, {"company": "FinIQ Pvt. Ltd.", "description": "Created template to generate implied & local volatility surface; improved calculations by 70% in excel VBA. Priced options using calculated volatility by Monte Carlo simulation and Black-Scholes-Merton formula. Calculated Greeks namely Delta, Gamma, Theta and Vega using the volatility calculated & Black-Scholes formula.", "endYear": "2013", "location": {"city": "Pune", "country": "India"}, "role": "Summer Intern - Quantitative Research", "startYear": "2013"}, {"company": "Student run Investment fund of IIM C", "description": "Managed a fund of INR 382,500, recruited and led a team of 10 junior analysts from the first-year students. Conducted sessions for PGP1s on Macroeconomics, Options & trading strategies, credit products and FX & rates.", "endYear": "2019", "location": {"city": "Kolkata", "country": "India"}, "role": "Fund Manager", "startYear": "2018"}]}, "status": "success"}

Candidate B Resume:
{"data": {"awards": [], "certifications": [], "education": [{"GPA": "7.0", "degree": "Bachelor of Commerce", "endYear": "2018", "major": "Financial Accounting & Auditing", "school": "University of Mumbai", "startYear": "2014"}, {"GPA": "", "degree": "Chartered Accountant", "endYear": "", "major": "", "school": "Institute of Chartered Accountants of India", "startYear": ""}, {"GPA": "", "degree": "MBA", "endYear": "2024", "major": "Operations (STEM), Finance, Entrepreneurship", "school": "The Wharton School, University of Pennsylvania", "startYear": "2022"}], "projects": [], "publication": [], "skills": ["Python", "R", "SQL", "Bloomberg", "GenAI", "Figma", "Microsoft Excel", "Google Sheets", "PowerPoint", "Adobe Creative Suite", "Salsa", "Bachata", "American Sign Language", "Turkish"], "workExperience": [{"company": "GiantLeap Capital", "description": "Spearheaded team of three to automate screening of public equities for potential PIPE investments; used Bloomberg and GenAI tools to reduce turnaround time by 95% enabling GPs to evaluate 10+ companies in time previously needed for one. Led financial diligence for $5M investment in IoT PaaS deal ($20M ARR); planned 18-month path to profitability with CEO. Facilitated Partners in designing investment thesis for $120B AUM marquee investor to convert them into anchor LP for Fund.", "endYear": "2023", "location": {"city": "New York", "country": "USA"}, "role": "Summer Investment Associate (Internship offer extended to Fall 2023 at Partner request)", "startYear": "2023"}, {"company": "9Unicorns Accelerator Fund", "description": "Sourced and evaluated 175+ early-stage deals; pitched to Investment Committee; negotiated investor rights/valuation for investments worth $32M+ (>20% deals) across 70+ startups; streamlined cross-functional workflows to reduce turnaround time by 33%. Portfolio Development and Strategic Planning: Conceptualized M&A opportunity for VideoVerse AI (Series B media SaaS); persuaded CEO to acquire target for inorganic revenue growth in USA; CEO gave formal acquisition offer at 2X of target's last round valuation. Identified and vetted candidates for CTO position at Hoopr (Series A music-tech); guided founding team to negotiate ESOP vesting. Formulated B2B GTM strategy for Hoopr pre-product launch; built sales pipeline; converted the first three enterprises to paid users. Advocated full-funnel marketing to onboard Indie artists on Hoopr; partnered with 92.7BigFM radio for joint promotion campaign. Presented five corporate partnerships to Fitterfly CEO (Series A health-tech); grew patient base for diabetes management by 10%. Explored content partnership for KahaniBox (Seed media-tech) with vernacular language translation tool; proposed viral marketing campaigns focused on price-sensitive users to increase paid users by 3X. Leadership and Firm Development: Collaborated with fund administrators to digitize 100% of fund operations post-pandemic; recruited and mentored ops team of two. Organized Demo Day for portfolio startups; developed next-round funding strategies; 28 out of 32 startups received uprounds. Congregated 100+ women in Indian VC industry; initiated 20+ conversations/webinars to improve access to network and deals.", "endYear": "2022", "location": {"city": "Mumbai", "country": "India"}, "role": "Senior Investment Associate (Offered Partner track role)", "startYear": "2021"}, {"company": "9Unicorns Accelerator Fund", "description": "Sourced and evaluated 175+ early-stage deals; pitched to Investment Committee; negotiated investor rights/valuation for investments worth $32M+ (>20% deals) across 70+ startups; streamlined cross-functional workflows to reduce turnaround time by 33%. Portfolio Development and Strategic Planning: Conceptualized M&A opportunity for VideoVerse AI (Series B media SaaS); persuaded CEO to acquire target for inorganic revenue growth in USA; CEO gave formal acquisition offer at 2X of target's last round valuation. Identified and vetted candidates for CTO position at Hoopr (Series A music-tech); guided founding team to negotiate ESOP vesting. Formulated B2B GTM strategy for Hoopr pre-product launch; built sales pipeline; converted the first three enterprises to paid users. Advocated full-funnel marketing to onboard Indie artists on Hoopr; partnered with 92.7BigFM radio for joint promotion campaign. Presented five corporate partnerships to Fitterfly CEO (Series A health-tech); grew patient base for diabetes management by 10%. Explored content partnership for KahaniBox (Seed media-tech) with vernacular language translation tool; proposed viral marketing campaigns focused on price-sensitive users to increase paid users by 3X. Leadership and Firm Development: Collaborated with fund administrators to digitize 100% of fund operations post-pandemic; recruited and mentored ops team of two. Organized Demo Day for portfolio startups; developed next-round funding strategies; 28 out of 32 startups received uprounds. Congregated 100+ women in Indian VC industry; initiated 20+ conversations/webinars to improve access to network and deals.", "endYear": "2021", "location": {"city": "Mumbai", "country": "India"}, "role": "Investment Associate (Post-MBA role, promoted from Senior Analyst position 12 months early)", "startYear": "2019"}, {"company": "Captitude", "description": "Envisioned and launched free e-learning app; first-of-its-kind; democratized access to structured learning content all over India. Designed app framework (Figma); collaborated with three engineers for development; crafted database of 1200+ questions and blogs. Promoted app in coaching institutes and CA firms; 1.1K sign-ups with zero marketing spend; transferred content rights upon exit.", "endYear": "2019", "location": {"city": "Mumbai", "country": "India"}, "role": "Founder", "startYear": "2018"}, {"company": "Vinod Dhankani & Co.", "description": "Assisted CEO in 15+ income tax, indirect taxes, audit, and real estate advisory assignments; led teams of three on bank audits. Devised and executed firm's foremost learning development program; conducted seminars on tax laws for 18+ associates.", "endYear": "2018", "location": {"city": "Mumbai", "country": "India"}, "role": "Audit & Tax Apprentice", "startYear": "2016"}, {"company": "Ernst & Young", "description": "Implemented tax advisory assignments for Fortune Global 500 companies.", "endYear": "2016", "location": {"city": "Mumbai", "country": "India"}, "role": "Tax Apprentice", "startYear": "2015"}]}, "status": "success"}

Candidate A Transcript:
{'pairs': [['Interviewer: Hello and welcome to the AI interview! This interview will consist of basic questions about your background as well as some high-level questions about the skills you listed on your application. Ensure that you minimize long pauses during your responses, otherwise you may be cut off prematurely. Are you ready to start the interview? \n', "Interviewee: Yes. I'm ready. Great.\n\n"], ["Interviewer: Great! I see from your resume that you've had significant experience in both quantitative finance and managing trading desks. Can you walk me through how you've balanced these two aspects in your role at CoinDcx, particularly in terms of the strategies you've implemented to manage risk while maximizing returns?  \n\n", "Interviewee: Uh, at CoinDCX, I set up that point division and the OTC desk from scratch. Uh, the strategies that I have, uh, built, uh, were built by me along with, uh, the developer who reported to me. Uh, coming to the balancing part of it, since I was also adding the OTC desk because I do have a stable experience in discretionary trading as well. Uh, so this is where in, uh, font was leveraged, uh, there as well. So it was a pretty good balance there as well. Uh, quantitative skill set came from my internship and my work at JPMorgan in their contribution. Uh, so leveraging those, I sort of build a symbiotic relationship between OTC and, uh, Wandersk at, uh, find DCX. And, yeah, that's pretty much it. It's\n\n"], ["Interviewer: It's impressive how you leveraged your experience in both discretionary trading and quantitative finance to establish the OTC desk. Can you elaborate on the specific quantitative models or tools you brought over from your time at J.P. Morgan to enhance the risk management capabilities of the OTC desk at CoinDcx?  \n\n", "Interviewee: Some of the skill set that I, uh, apart from JPMorgan were about the model building, uh, specifically around the pricing of the pricing of the things. For example, BTC, uh, does not follow a typical, uh, normal distribution or a or a black shows model. We use something called SVCJ, stochastic volatility correlated jump, to price in, uh, options on BTC. Although we didn't trade options at 0.86, but, uh, information in options were were pretty much relevant there. The other things that I sort of, you know, uh, helped other thing that helped me here at Point ECX in Quant was, uh, my experience with order books. So a lot of time because Point ECX is is a stand alone, uh, exchange in India. Uh, Indian market sort of operate differently from the rest of the world. There is some sort of, uh, premium that goes on in here. Liquidity is a bit different. So trading those, uh, liquid illiquid options and payers, uh, understanding of them sort of help me here build those strategies, uh, to sort of, you know, exploit the alpha and, uh, get the ball rolling for the team. Uh, although I say alpha and get the ball rolling for the team, most of the work eventually that we ended up building were, uh, market neutral and, uh, market making strategy rather than alpha exploiting. Given\n\n"], ['Interviewer: Given the non-normal distribution of BTC and the focus on market-neutral strategies, how did you adjust your model to account for this, and what were some of the key challenges you faced in implementing this non-standard modeling approach in a practical trading environment?  \n\n', "Interviewee: Uh, issues that we faced, uh, effectively, what happened is, uh, BTC let's say, in an international market, a BTC USD or a BTC USD pair, in itself, modeling that is a bit difficult. But, uh, there are no liquidity challenges there. Uh, modeling it there is sort of like introducing a jump parameter in our modeling of, let's say, simple black shows or a, uh, stochastic, uh, black shows equation. And then maybe we can go from there. Uh, but a challenge that Indians or Indian market would face is, uh, it's built upon a upon a stable coin premium. So you have to, uh, account for that. So that that requires, uh, 2 form of understanding of things. 1 would be a stable coin premium plus the underlying, uh, underlying b t BTC issue. Now to tackle those challenges of the non normal distribution, uh, we sort of had a look at the actual BTC prices or the actual BTC auction prices that is where they are at. Uh, so compared with normal, what we have observed at BTC distribution, uh, compared to a normal or a bell curve is is is sort of, like, longer, uh, at the center, but thinner at the center, and the tails are thicker. So what it means is BTC doesn't move much, but when it does, it does, uh, jump a lot. So we just made a few changes in our strategies accounting for that. Uh, one example would be that, let's say, when I'm trading, uh, for market meeting, and if the spreads are normally in a in in in a very stable or a mature market, the spreads change basis volatility when our market making would would largely follow some form of normal distribution. But at Bitcoin, the spreads, uh, versus volatility, uh, at a very low volatility, we could keep, uh, spreads lower and make money. But as soon as, uh, volatility increased slightly, the spread that we adjusted for were pretty high. So this is an explanation for the thing, uh, for the change in the normal distribution of Bitcoin, uh, versus a traditional asset wherein, you know, the the the the tails are thicker and the centerpiece is thinner and longer. Your\n\n"], ['Interviewer: Your detailed explanation highlights the complexities of modeling and trading BTC, especially in the context of market dynamics and liquidity variations. How did you quantify and manage the stablecoin premium and its impact on trading strategies, given its significant influence on certain pairs?  \n\n', "Interviewee: Uh, we have a pair called USDT INR, Tether INR, which is, uh, the most liquid in Indian markets. Uh, what we had come to realize is the the stable coins premium with, let's say, a dollar INR, uh, value is is is function of, uh, three things, basically. 1 would be your regulatory enrollment. So whenever there is some form of, like, regulatory uncertainty or people who want to exit, uh, crypto market in general, uh, that premium sort of shrinks. The other would be, uh, whenever, uh, there is demand to buy BTC in India relative when I say demand to buy, relative to the rest of the world. For example, there may be a selling pressure in the, uh, rest of the world, but let's say the selling pressure or the selling volume or the selling intensity is Indian low. In that case, that premium would expand because it makes sense for people to actually buy USDT and then use that USDT to sort of, like, buy, uh, BTC. So that premium is sort of like a vote on India's propensity to hold, uh, crypto in general versus the rest of the world. Uh, and adjusting for some form of regulatory and the friction in moving, uh, money around. So let's let me give an example. Let's just say the regulatory and the friction. Like, uh, INR is one way convertible currency. Uh, that, uh, regulatory friction and the other thing, let's say, it account for 2 and a half percent premium. Anything more than 2 and a half percent in my model or in my trading, I would attribute it to the India India population's propensity to hold cryptocurrencies in general. So that sort of, like, is the word on that. Uh, one of the example is whenever there were regular regulatory uncertainties in India about banning crypto or, uh, stuff like that, uh, a year or 2 back, uh, this this actually premium turned into a discount. It traded below the INR, uh, USDT market. So there are qualitative aspects and quantitative aspect both in managing that premium of stablecoin. Under\n\n"], ['Interviewer: Understanding the factors influencing the stablecoin premium in the INR market is crucial for effective trading strategy. How do you approach the integration of qualitative and quantitative factors into your models or trading decisions, especially in situations where qualitative aspects may not have clear numerical representations?  \n\n', 'Interviewee: Uh, this is wherein, uh, we had, uh, we took help from Offshik. This is wherein, uh, they we they they, with the health analytics, had built some form of scraping from Twitter and other things. And, uh, we adjusted our parameter based on those, uh, feedbacks. But everything qualitative, uh, whenever we cannot quantify we could not quantify it, uh, had to be sort of, you know, tinkled with parameters manually. Whenever we could quantify it, for example, if the premium is going down, uh, we know that, you know, the demand, uh, for BTC, uh, in India is, uh, compared to the rest of the world, is slightly less. We can, uh, sometimes, uh, you know, uh, go short on a BTC thing. If if premium is expanding pretty aggressively, we could go long on BTC because everyone is going long. But a lot of those qualitative factor, which cannot be quantified since you asked about non quantifiable qualitative factor, I had a whole, uh, team of traders. Uh, think of those traders as a sell side bank test wherein they run their OTC desk. So we had inventory and warehousing of things, and those qualitative factors were taken care by my team from that part of the team. So I had 2 teams. 1 who was doing solely quantitative stuff. The other were discretionary trader, uh, who sort of would take care of, uh, qualitative aspect for the team.\n\n'], ["Interviewer: Integrating sentiment analysis from sources like Twitter is innovative for adjusting trading parameters. How do you ensure the reliability and accuracy of these qualitative inputs, especially when they're used to adjust trading strategies significantly?  \n\n", "Interviewee: Well, we didn't use we didn't we didn't rely on Twitter significantly, uh, specifically for, let's say, our mainstream points. Uh, whenever there was now to make sure that, you know, the the the sentiment analysis or, uh, whatever we are doing, why Twitter is not giving up on, What we did is, uh, we sort of looked at the real world signal as well. For example, when there was, uh, BTC, ETF news, there were a lot of preemptive sentiment, uh, on on Twitter, which had to be checked with other, uh, mainstream news sources as well. So once our Twitter thing, uh, is there, if we we didn't go all in on the trade, we just, uh, were sort of, like, cautious and, you know, maybe initiate a little bit of trade and be, uh, be ready to, you know, get a bit, uh, higher quantity trading in in in in the market. But once as soon as the news is, uh, sort of, like, confirmed by one thing or the other, uh, we sort of, uh, jumped into a trade. So Twitter was just one of our parameter. Uh, sentiment from website scraping and other thing is just one of one of one of the parameter. Then have very large weight. Uh, it become important around certain, uh, turning point in the market. This was not always relevant. For example, when there's a huge sell off, uh, when, uh, when there is a very large open interest, let's say, around, uh, option expiry or even there is when the funding rate between futures and BTC, uh, b BTC spot is pretty high. In that case, we we felt that, you know, uh, this could be relevant. Under normal circumstances, that didn't play much into our trading. Given\n\n"], ['Interviewer: Given the critical role of qualitative factors in your trading strategy, how do you envision leveraging AI or machine learning models to better incorporate and perhaps even automate the adjustment process based on qualitative inputs?  \n\n', "Interviewee: Input that option he needs to do. Uh, I think it's pretty easily, uh, uh, can be pretty easily handled by, uh, by an AI model. For example, a lot of scraping that is done is simply a code, uh, which can be scrapped. This is that, uh, an AI model can simply, uh, sort of, you know, uh, have some form of qualitative score score, something like a greed or a fear index that we have for equity market. That sort of index can be built by an AI model, which obviously can be refined as, you know, uh, it gets trained over more data, uh, to to figure that out. How that reacts, how that greed and, uh, fear index once we have derived that greed and fear mix via AI, how that is leveraged, uh, with the actual actions in the market. For example, when the market is pretty much, uh, to the greed side, and let's say, uh, how how are the jumps looking? So in that case, my assumption this is how we did it qualitatively was every downward down should move down jumps were pretty, uh, aggressive and swift, but the up moves were, uh, sort of, like, drifting in nature. So that sort of thing with AI can pretty easily be done. I think, uh, the whole qualitative aspect of things current pretty much, uh, the level that we were doing can pretty much be automated by an by an AI. The only place I think human, uh, touch would be required is via the experience or, uh, the parameters of the area that AI is not looking at. Because, uh, as I said previously, there are times when one factors are, uh, you know, very important as compared to the other. So PB and AI would probably take time on training, shifting from one driver to the other driver. That's the only challenge AI would face with regard to, uh, qualitative factors. So AI plus sort of like an human observer, uh, is pretty much, uh, something that can be replaced whatever we were doing with, uh, our strategies. Consider\n\n"], ['Interviewer: Considering the potential for AI to handle qualitative aspects, how do you plan to ensure the interpretability and transparency of these AI models, especially in an industry like finance where understanding the "why" behind a decision is crucial for trust and risk management?  \n\n', "Interviewee: Start, uh, one one way to build trust, uh, especially in in an in a Fintech or a finance sector would be to have an, uh, human observation. Maybe you can, uh, over a period of time. The second thing is backtesting the AI itself, like, which, uh, which are the turning points. Just like any other program or any other coding, uh, things, I think there are gonna be tail cases. Uh, identifying those tail cases could be pretty important. Again, uh, this is very in a very large set of data, at testing things, where the things are going haywire, uh, can come into the picture. We can add a layer of, uh, permission, uh, if AI based decisions are there. For example, not going beyond, uh, certain things, and we can have a strict stop loss. So my point is we already have, uh, CTAs or algorithmic trading, right, uh, built into the system. And AI is sort of, like, expanding that domain with lot of qualitative factors, uh, maybe quantifying them into, uh, an, let's say, a greed or fear index or some form of rankings, uh, to sort of, uh, enhance those. So just like our, uh, algorithmic trading or height, uh, high frequency trading has happened, has evolved over a period of time, it doesn't always make money. Sometimes there is a need for a human observer to make sure the risks are, uh, managed correctly. But, uh, AI greatly enhances, uh, the abilities of that human observer wherein, you know, the datas are readily available, uh, to be to be seen, like, what's the exposure and, uh, the other things. How do you\n\n"], ['Interviewer: How do you envision the role of AI in dynamic risk management systems, especially in terms of its ability to identify and respond to emerging risks in near real-time?  \n\n', "Interviewee: Be really interesting. Like, a lot of risk management that happens are rule based in in financial firms and other places. For example, when your leverage goes beyond these things and, you know, like, focusing on sudden risk, for example, currently, I would say, uh, Israeli run risk. I think a lot of equity managers would be or crypto managers would be paying attention to. These are all, uh, I mean, pretty much, uh, lot of things that are done by human can be done by AI. And lot of decision makings, which are simply rule based. Uh, lot of those decision makings are if then else in nature. For example, this is the risk. We calculate bar and we say that, hey. We need to reduce the risk. And then this is where in in AI, which, let's say, our trading program can come into picture and opportunistically can reduce the position. Keep an eye for the hedges if so a lot of, uh, these things at at least at at older or, you know, of of of of traditional houses that happens, most of it are if then else, uh, system monitored by a human being. So that is, I'm guessing, pretty much easily automatable, uh, by by an AI. The benefit with AI as compared to a traditional coding would be, uh, the longer it can run, the longer the better it can get trained, uh, which probably was not there with the traditional, uh, coding or something like a conditional, uh, coding, uh, previously. As\n\n"], ['Interviewer: As AI systems learn and adapt, how do you propose to manage the potential for AI to develop unexpected behaviors or strategies that may not align with the intended risk management goals, especially in complex and interconnected financial markets?  \n\n', "Interviewee: So for example, this is not a new problem, to be very fair. Uh, this problem, I think when we started, uh, I think when I say we, when humans started, uh, with the algorithm trading, this was there with algorithm trading as well. How do we manage, uh, the sizes of the position, the leverage, or the the adverse, uh, potential of losses? And this is wherein we we came up with a rule based system. We came up with, uh, systems wherein, you know, limiting the AI in certain cases. So I think those conditions can automatically be in built. Uh, I don't see it different than a human trading. For example, when a trader a human trader is taking position, there are certain limits on it. You cannot go beyond this risk. You cannot take this many positions. Uh, the net position or the net d v zero one or sensitivity or whatever drivers cannot be higher than this one. This can be sort of, like, uh, implemented within AI system as well. The other thing is at least till the time AI is training, we can have, uh, 2 order of, uh, sort of, like, uh, lock locking mechanism wherein, you know, once, uh, if something goes on in the first order, the second, uh, code or a second form of a technology can take care that, you know, the systems are, uh, working properly as in, you know, uh, the strategy stops there itself. We have in I've got in trading as well today. Uh, let's say if your losses go beyond a certain limit, the the strategy, squares off everything, stops for a while, sends a notification to a a trader or a manager, sending them, he takes the further decision. So lot of, uh, the problem that, uh, we we discussed are, uh, sort of existing for a volume trading as well and, uh, taking care of there as well. How do you\n\n"], ['Interviewer: How do you plan to ensure that the AI systems remain transparent and auditable, especially in the context of regulatory requirements and the need for stakeholders to understand and trust the decisions made by these systems?  \n\n', "Interviewee: System or, uh, like, decision making system, the logs for the same should come in handy. Today so this is equivalent to, uh, SCC regulation for, you know, banks to to have those communication happening through community challenge so that when whenever that happens, those, uh, communication can be audited that were sort of, you know, useful, uh, especially in things like lab or stand alone, nothing. Same way a lock of actions of, uh, of AI decisions can be maintained, uh, wherein a lock can have the action and the reason for the action. And then let's say a size of the trade and, uh, stop loss and take profit for the for for all the things. And, uh, that logs can be updated, uh, with the decision. For example, you had a trade and the logs should be updated, like, why did you took, uh, take profit? Why did you change something? What are the relevant details that has happened? So, uh, a log can have, uh, sort of, like, a water flow model wherein, uh, one scale would be sort of like the summary, the list of the trades, and other thing. You can click on that rate to see, like, what were the basic detail. And then just like a blockchain, it can have other details, like, uh, during the life cycle of trade, what what all things had happened. And, uh, if there are losses, like, uh, one one can have another look, what could have been done well, and, uh, what would, uh, ideal situation would have demanded, when should have the book losses. So, uh, sort of like a equal into a blockchain ledger, you can have a decision making ledger for a for an AI, for regulatory requirement, and for the transparency. Like, whatever has been done, why it has been done. Thanks.\n\n"], ['Interviewer: Thanks for taking the time to interview with us! The time allotted for our interview has come to an end. We will reach out to you with next steps shortly. Please click the end interview button to end the interview.', 'Interviewee:']]}

Candidate B Transcript:
{'pairs': [['Interviewer: Welcome to the AI interview! This interview will consist of questions about your resume and a case study style question to assess your technical skills. Minimize long pauses during your responses, otherwise you may be cut off prematurely. Are you ready to begin the interview?\n', 'Interviewee: Ready. \n'], ['Interviewer: Great! Can you elaborate on the automation project you spearheaded at Giantleap Capital for screening public equities, and what technologies you used?\n', 'Interviewee: I was working with a team of 3, and we were using chat GPD, Bloomberg, and Claudia. \n'], ['Interviewer: How did the automation of public equities screening impact the overall efficiency and decision-making process at Giantleap Capital?\n', 'Interviewee: To basically augment So we were able to use these tools to reduce the turnaround time that we needed to screen 1 company. From, I would say, more than a week to 2 days. This enabled us to screen 10 companies in the same time so that the general partners could evaluate more companies in the same amount of time. So it quickened the pace of decision making it was also very efficient in terms of where we put the number of hours of a day Instead of doing work that AI can do. \n'], ['Interviewer: That sounds impressive. Could you provide more details on the methods or criteria you used for sourcing and evaluating early-stage deals at 9Unicorns Accelerator Fund?\n', 'Interviewee: Sure. Most of the sourcing was network driven. So I would reach out to people in my network network, both start up founders, other venture capitalists, people who graduated from these schools people who were switching jobs to understand if they were starting something on their own, if there was any interesting opportunity that were Interesting enough to look at. And I was also speaking to VCs who were late stage VCs who had passed on a opportunities because they were too early for them. And we were looking at all of those opportunities to source start ups in the early stage across sectors. Yep. \n'], ["Interviewer: It's interesting how network-driven sourcing was for you. How did you go about pitching these early-stage deals to the Investment Committee and what key aspects did you focus on during your pitches?\n", "Interviewee: So the main key process to pitch and pitch a company to the investment committee was after we conducted a very deep due diligence. I had calls I had multiple calls with the start up founders to understand the business model, the strengths, the competitive landscape, how they were benefited to start this project or company? What was the USP of the team? And then I designed pictures which were basically investment memos that was sent to the committee before a Zoom call was planned. With 4 to 5 different startups pitching at any given point in time. So it was a 1 hour meeting in which 4 to 5 startups used to come and pitch like, maybe 10 minutes to the investment committee. I'm sorry. What was the follow-up question that you asked? \n"], ['Interviewer: Thank you for the detailed explanation. My follow-up is: Can you describe how you streamlined cross-functional workflows at 9Unicorns Accelerator Fund and the specific tools or techniques used to achieve this?\n', 'Interviewee: Okay. So at 9 unicorns to complete a deal closure process, right, from sourcing the deal until signing of the SHA. Is the shareholders agreement. There were at least 4 teams involved in any deal. These included the investment team at the fund, the lawyers, the transaction advisers who were doing financial due diligence and background checks. The fund administrator administration fund, team, which was involved in making sure that the check is issued to the right team. And, yeah, I think they were these. So I basically built a tracker and worked with all of these teams to make sure that one, we follow-up process flow. So in order to make sure we do that, at that point in time, we were using Google Sheets. Which was based and designed in a way that every team gets a notification when a task is due at their end So this made sure that we did every task and this reduced the time from closing a deal in 45 days to just 15 days. We were working on tools and techniques like Google Sheets, Calendaring. A lot of, I would say, email trails that were set into place. There were formats and criteria set into place. And any other tool that I can think of No. Mostly these. \n'], ['Interviewer: Thank you for sharing those details. Now, moving into the case study portion: Imagine you are tasked with creating an AI-driven tool to evaluate early-stage startups for potential investment, leveraging your experience and the technologies you mentioned. Can you describe the high-level solution architecture or approach you would take to build this tool?\n', "Interviewee: Very interesting. So I would divvy up So into 3 parts for early stage investing. Analyzing the market, analyzing the team, analyzing the product. These are the 3 key things that I would look at when evaluating an early stage investment, especially the team. Now how would an AI do this? I would think that the market piece would be very easy to build On EA. So when I say market, it's basically analyzing, 1, is there a need for the product? 2, how big is the market? 3 what is the competitive landscape that looks like? Say for example in India, or US. For a market For example, pet supplies. So all of these things can be AI built or driven Once we can set up information sources that the AI can learn From Now these sources could be anything and everything right from consumer behavioral changes, how is the market moving, what are the revenue streams looking like in some industries, Do we see a niche popping up? Do we see any new I would say, yeah. So these things. The second piece, which is the team, I think is something that I would most rely on a human To evaluate, when I say team, it's so after doing a basic check, which again can AI can do, which is what's the team pedigree, where what's the education background? What's their professional qualification? Why are they best suited to start this company, etcetera, and what's the dynamic of the Team's truck structure, what is the equity split, etcetera. All of these things can be automated, but what I would rely a human on for is gauging the personality, the preferences of how they've worked with each other, Are they hustlers? Do they have it in them to start a company? Do they have it in them to pivot? Because a lot of early stage companies pivot. So all of those things. And the last piece is the product. How will I use AI for the product? Depends on what the product is. If it's a software or a tech product, I'm sure we can set up AI tools to test it. To understand the metrics, the user acquisition, what's the MAUD you like? What's the usage frequency, like, etcetera. But if it's consumer product, say, for example, a sunglass, I would have to think of more ways to incorporate AI in the evaluation process. Sorry about the long driven \n"], ['Interviewer: Thank you for your thorough approach. Could you identify potential challenges or constraints you might face when implementing this AI-driven tool, particularly in evaluating early-stage startups across diverse industries?\n', "Interviewee: So as I mentioned, one is the biggest piece of the puzzle is the team in early stage investing, and I'm not sure on how we can use AI to analyze the personalities of a person and, like, the behavioral behaviors of a person, their decision making skills, are they hustlers, also the dynamic between the cofounders, think that comes, at least to me, It came after multiple conversations, And getting a knack of how to analyze a founder's thought process. I'm sure we can build an AI around it. But it might take time. So it is a challenge in terms of AI is built on reinforcement learning or structured learning But those learnings are based off of Like, I would say inputs, But sometimes you do not have enough inputs to drive these decisions at a artificial intelligence level. That's one. The second is in terms of market, AI can build on inferences based on existing trends, But a lot of times, early stage investments are based On a trend which is new. So there is no past history or data. To infer from. So I think that could be a limitation When judging startups from an AI perspective. Also, I think biases So already we have a lot of biases So start ups founded by women founders, for example, or people of color. I don't know if there's any bias that is baked into AI models that could let very good opportunity slip out of the gaps. Because of inbuilt biases. You might have heard of the Amazon's AI Resume Screener. That had very strong biases built inbuilt Within So, yeah, something to fight that. \n"], ["Interviewer: Great points you've raised there. What do you think would be the potential benefits or impacts of successfully implementing this AI-driven tool for evaluating early-stage startups?\n", "Interviewee: I'm definitely sure it will save a lot of time. So all of that time can be redirected towards basically evaluating more opportunities or working with these startups to actually grow the businesses. So a lot of sourcing and evaluation part of the job converts into portfolio development, operational efficiencies that VC can help bring on the table. That's one I think another benefit is that AI could actually spot trends that humans fail to spot. Definitely, it has been proven. I think AI is 25% better at predicting not 25%, 25 times better at predicting than a human. And less prone to error. So we will definitely be more it could be a scenario where we invest in more profitable or more investable businesses so there are less fallouts from a portfolio for a VC. So, yeah, these are the few benefits that I can think of. \n"], ['Interviewer: Given these benefits, what would be the key steps or milestones you would prioritize in implementing this AI-driven tool for evaluating startups?\n', "Interviewee: I think in terms of milestones to implement, I would definitely think so. We already used tech tools like Affinity to keep a track of sourcing and networking. I think, if I add a layer of AI, we can therefore, the entire piece of analyzing markets and analyzing trends to AI. So I would say that would be the first milestone for me. So if I categorize analyzing any early stage opportunity into market, product, and team, I would defer the market piece to AI, and that would be the first milestone I would work with AI on. In terms of the second milestone, I would think about how can I automate the day to day mundane tasks and defer it to AI? So, as I said, early stage VCs are all about networking. So, basically, knowing about the right deal at the right time and getting into that deal. And getting a bite of it. But for that, we need to do a lot of networking, setting up calls, meeting people. I think a lot of scheduling, calendaring can be 4 2 ai. That already is Done. To a a very large extent. In terms of product, I would also, therefore, I would say like so say, for example, there's a SaaS matrix. You can set up Benchmarks to analyze, okay, is this deal even worth my time? By putting in user base or revenue numbers or churn or what's the repeat Customer intake, etcetera. So all of these things can again be automated and Therefore, to AI in terms of a milestone. Yeah. \n"], ['Interviewer: Thank you for outlining those steps. Can you discuss potential trade-offs or compromises that might need to be made during the implementation of this AI-driven tool?\n', "Interviewee: I'm thinking. Sorry. So, I would say compromise would be 1, we might be favoring something that has been proven to be successful in the past, and we are expecting it to be successful in the future as well. So for example, my criteria of team product and market. That criteria might be redefined over time. But AI might still keep rewarding startups that have great team product and market. But there might be a 4th criteria that might just stem up. Say, for example, AI capabilities. And because we have not built that into our learning database, the AI is not inferring from that new criteria that being built. That that could be a compromise. That we might have to sign up for. \n"], ['Interviewer: What do you see as the opportunities for future enhancements or expansions of this AI-driven tool once it is successfully implemented?\n', "Interviewee: I think the opportunities that I can imagine are more on the synergistic side of things. Right now, VCs operate mostly in silos. When they're evaluating opportunities. So one opportunity is very unique and boxed out from another opportunity. I think AI will be very smart to info from multiple investments of the same VC or of different VCs to infer how we can synergistically Improve business models. So it could be somewhere like, okay. These 2 companies partner where one is a supplier, one is a contractor. Or a client, and they can increase their revenues just by that partnership. But AI would be the first one to infer, okay. This Square peg goes into this square hole. And I think that is an opportunity that humans may not be as fast to leverage as an AI could be. Given they have the right criterias to analyze opportunities. \n"], ['Interviewer: Thank you for sharing those insightful responses. Now, how would you approach integrating this AI tool into the existing workflows at a venture capital firm to ensure minimal disruption and maximum adoption?\n', "Interviewee: Firstly, I would imagine that every workflow would basically come on one platform. So every stakeholder of the VC interacts with the same platform. Platform. So one, we have everyone on the one on one platform working So right from scheduling meetings, setting up calendar invites to, signing the shareholders agreement. Everything happens on one platform. So one is bringing everyone on the same page. 2nd, in order to not disrupt, the workflow, anything that is going on so AI has access to my meeting notes, to my video interviews, to my Zoom calls with founders. So every data point is still in the same place that I log. But AI has access to it so that it can infer details from it. That's pretty easy, I would say. In terms of analyzing founders, I'm sure there could be base to understand how a person thinks. By giving them case studies or by giving them, by asking more around how they took decisions in their businesses. And we can have AI also coanalyze or copilot with the venture capitalist Okay. Is this a person I think is a hustler or is this a person I can rely on in terms of crisis decision making? So we can, like, copilot the entire process. That is one way to not disrupt any existing workflow, but have AI come in and partner in every step of the way. \n"], ['Interviewer: What metrics or key performance indicators would you monitor to evaluate the success of this AI-driven tool once it has been implemented at a venture capital firm?\n', "Interviewee: So the KPIs could be as basic as okay, is my analysis and AI's analysis like, how correlated are they? When I talk about analyzing the team? So are my inferences and AI inferences correlating? If there's a high positive correlation, that means the AI is learning how to analyze and evaluate a team. For markets, if the AI is able to spot trends in time, So a KPI could be how accurate the AI is in terms of analyzing and spotting trends. So we can again, so this is a hindsight analysis or KPI. That okay. For the 1st 6 months, the AI analyzes trends For the next 6 months, we see if those are accurate. If they are, that means the AI has actually performed well on that KPI. So that could be one KPI. And the last KPI in terms of product could be how strong is AI crunching numbers? So if the revenue and SaaS matrix, for example, are crunched down by AI, and they can forecast. Okay. This is the forecast for the next year's revenue. And if those forecasts are correct, again, accuracy could be a KPI here. So these are the few things that come to the top of my mind. \n"], ['Interviewer: Thank you for taking the time to interview with us! The time allotted for our interview has come to an end. We will reach out to you with next steps shortly. Please click the end interview button to end the interview.', 'Interviewee:']]}

Preferred Candidate: