My Journey from Econ PhD to Tech — Part 2: Interview experience
By: Scarlet Chen
Tags: Networking, Job Hunting, Data Science, Economics, Tech

Uber

On the same day as I applied for the job, i.e. Aug 17th Mon, the recruiter reached out to me to schedule a chat. We chatted on Aug 20 Thu, where she told me about the structure of the data science team at Uber. The next day, Aug 21 Fri, I received confirmation for my phone screen, which was scheduled for Aug 26 Wed.

The first phone screen is always the hardest — I still had no idea what a tech data science interview looks like, and what they were looking for. Also, Uber’s interviews are heavily case study, which is the most fun, but can also be the hardest, because case studies are very open ended. I thought I did terribly, and went into a bet with Anthony on whether I passed, which I failed — on the same day, the recruiter called to say I passed, and that I’ll be invited to a second phone screen.

At that time, I thought what happened was that I did so badly that they decided to add a phone screen, which turns out to be completely wrong — firms don’t change their processes for individual candidates. I’ve indeed heard of cases when someone had 3 phone screens, but it’s usually because of something on the team/firm side, rather than because of the candidate.

The next day, Aug 27 Thu, I received confirmation for my second phone screen on Sept 3 Thu. — if I had asked for it to be scheduled sooner, I’m sure it would have happened; at that time I wanted it to be scheduled for later, because Uber was moving too fast compared to my progress with other firms.

On Sept 3 Thu I had my second phone screen, which went much better — the second one is basically the same as the first one (after the onsite I realized that all interviews at Uber, either phone screen or onsite, are basically the same, i.e. case studies). The interviewer even asked ‘have you done an internship before?’ (I don’t) because she was surprised by how practical I was.

On the same day, the recruiter called again to say I’ve passed the second phone screen, and collected my availability for the onsite. On Sept 4 Fri, I received confirmation for my onsite for Sept 11 Fri, i.e. 1 week later.

Notice how amazingly fast things were — if you don’t think it’s fast, don’t worry, you’ll realize it by the time you read my experience with Facebook and Google.

On Sept 8 Tue, I had a super detailed prep call with the recruiter on what would come up in the onsite — most firms do this with their onsite candidates, but firms differ on how detailed and how accurate the info are. Uber is on the detailed and accurate side — I’ve had cases where what the recruiter said is completely different from what I had.

The recruiter was on vacation from Sept 11 Fri, i.e. my onsite to Sept 14 Mon, so I only got to know my result on Sept 15 Tue: I got the offer! The first offer is always the most relieving because you know the rest is cherry on top. — which turns out to be not true, because having multiple offers is extremely important for wage bargaining. I highly recommend you not to end your recruiting journey after your first offer, even though all parts of you are screaming ‘I want to be done!’ at that time.

On the whole, the Uber recruiting experience was extremely smooth — the recruiters were super fast and responsive and know what they are talking about. The interviews were fun and I learnt a lot. All the interviewers were friendly. The hiring manager was extremely supportive — always there to answer my questions regarding the recruiting process.

The only thing I did not like is that they are on the more pushy side in terms of deadlines: when they told me my offer details (i.e. $$$) on Sept 18 Fri, they didn’t say there will be a deadline. But on Sept 24 Thu, the recruiter suddenly scheduled a call to tell me that Oct 9 Fri is the deadline. Now that I think about it, it’s probably because, in the meeting with the hiring manager’s manager on Sept 22 Tue, I said that ‘with high probability I’ll be signing’, and I told the hiring manager that the only other interview process I’m in at that time was with Amazon and my onsite will be on Oct 2 Fri.

This shows one of the most important principles when managing your recruiting process — don’t share too much information! Information = power. Whoever has more information has more power. In the recruiting process, the firm has the upper hand: you’re usually very young and naive, answers whatever question the firm asks such as ‘what’s your progress with other firms’, ‘what’s your salary expectation’, ‘what do you feel about the offer’, ‘when can you make a decision’, etc. You really need to protect your information, and get as much information from the firm as possible, e.g. ‘what’s your progress with other candidates’, ‘what’s the salary band for this role’, ‘how’s my interview performance — can you share the notes with me’.

Back to Uber — with a bunch of pushing, they not only canceled the Oct 9 deadline, but did not set a new deadline at all. Because of the Oct 9 deadline, I had to expedite my interview process with other firms by a lot, and eventually had 4 back-to-back onsites on Sept 30 Wed & Oct 1 Thu (Zillow, split into 2 days), Oct 2 Fri (Amazon), Oct 5 Mon (Facebook, Novi), Oct 6 Tue (Google). Back-to-back onsites aren’t a good idea — towards the end, I can’t even remember which examples I’ve used for my behavioral questions that day.

That’s why I’m telling you that all deadlines are fake, unless there literally is another candidate who will sign her/his name on the contract tomorrow if you don’t do so today, which is almost never the case. If a firm says ‘you have to decide within 5 days/1 week/2 weeks of the offer’, just say ‘This is a really big life decision. I’m afraid that this timeline won’t work for me at all. I’m still in the process with a few other firms I’m equally excited about, and I’d like to have all the information I need to make an informed decision. I don’t want to go back on my own words, so I don’t want to commit pre-maturely.’ — It costs a firm ~$25k and 45 days to arrive at the offer stage with a candidate, so once the offer is in your hand, you have the upper hand, and it’s them that don’t want to lose you.

Zillow

If you think the Uber timeline is amazingly fast, check out my experience with Zillow: Sept 24 Thu initial chat with recruiter, Sept 25 Fri phone screen, Sept 30 Wed & Oct 1 Thu onsite (split into 2 days), Oct 1 Thu offer.

As I mentioned in the previous section, I talked to XN, a Senior Applied Scientist at Zillow Offers in late Aug. Even though there weren’t any positions for my level at that time, he said he will let me know as soon as something opens up. For most people who had said something like that, they never followed up, but XN did: in mid Sept, he reached out to me to say a position that fits my profile will open up soon, and asked if I’m still available. I said yes, and he passed my CV to the hiring manager. On Sept 24, he said that the hiring manager has expressed interest. The rest is history.

The reason why Zillow was moving so fast was that I’ve let them know my Oct 9 Fri Uber deadline + the hiring manager was very interested in my profile (they are a team of ML specialists looking for an econ phd with domain knowledge in housing) + the recruiter was really awesome.

On the content front: the role that I was applying for is called ‘Applied Scientist, Machine Learning’, so not surprisingly, there are ML questions, but still presented in a case study fashion — they would present you a real problem (or a stylized version of that) that they were solving, and ask what you would do. It was super fun — probably tied with Uber in terms of fun-ness. The hardest coding question I had in my entire recruiting journey was probably also with Zillow (maybe tied with Facebook Novi). Even though I wasn’t able to solve the whole thing, the interviewer was happy that I got the spirit of it very quickly after he gave me a hint, which to me was also a positive signal — they care more about my problem solving ability, less about the technicalities.

Coursera

Remember that I said the same week when I applied for Uber, I also pinged a number of my contacts to say ‘I’m ready to apply, please refer me’? EN was one of them. I got to know EN in the department’s ‘job market candidates panel for tech’, and he generously referred me to all 3 firms he worked at before, and 2 of them turned into offers.

On Aug 20 Thu, EN wrote an email introducing me to EY, a very senior person on the Data Science org at Coursera. I was both surprised by how high up the connection was, and also by the fact that she offered to chat — we talked the next day, Aug 21 Fri. Next Mon, Aug 24, the recruiter reached out to collect my availability for the phone screen, which was later scheduled for Sept 2 Wed.

I thought I was interviewing for their full-time position, but at the end, the interviewer (who also turned out to be my hiring manager/manager) said, ‘You did great! I can offer you an internship right now, or we can re-connect next spring when we know our full-time headcounts for 2021.’ Lucky enough, I’ve already learnt the most important lesson for job hunting — always say yes, whether it’s a connection, or a referral, or an interview. I gladly took the internship offer, and started a few weeks later on Sept 21 Mon.

Even though the internship didn’t eventually convert into a full-time offer — during the midterm review, DN (my manager) said I did great and if they had headcounts they’d give me an offer, but they still don’t know their headcounts yet — the Coursera internship was still an extremely correct decision: practically speaking, I got to know what it actually looks like working as a Data Scientist in tech, and got experience with basic tools such as SQL and Python — interview prep and real work is still different: in interviews, my SQL code was 5 lines long; at Coursera, they were 50–500 lines long. But more importantly, I just had a really really good time — my team were awesome and I enjoyed talking to everyone — the ‘weekly hang’ was the thing I looked forward to the most every week during that two months.

Quora

Anthony not only posted on his Twitter about my job search, he also pinged his friends for referral, one of which is someone whom he got to know during his Facebook time who later worked at Quora, who then introduced me to his friend, WN, a Data Science manager at Quora. (This is similar to how I got to know JN at LinkedIn: Anthony introduced me to his friend GO, who later introduced me to him.)

Out of my surprise, WN directly referred me for their new grad Data Scientist position, which technically hasn’t been posted yet, so I again front ran the market (similar to what happened with Zillow).

So again, things were moving really fast: I got introduced to WN on Aug 23 Sun, and she replied on Aug 24 Mon saying she has referred me, and on Aug 25 Tue the recruiter reached out to me to hand me the data challenge. On Aug 28 Fri, I submitted the challenge, and the recruiter said they’ll follow up shortly. On Sept 2 Wed, the recruiter said I’m invited to the phone screen. On Sept 8 Tue, I had my phone screen. The next day, Sept 9 Wed, the recruiter emailed me to say I’m invited to the onsite. On Sept 10 Thu, I received my confirmation for onsite for Sept 17 Thu. The only slow down was after the onsite: it’s only after 12 days, on Sept 29 Tue, that I got the offer.

My guess was that, given that they are a small firm — a total of ~200 people, with ~15 people on the data science team, they had to be selective about offers, so they probably interviewed a bunch of people before they decided on who to give offers to. Also, it takes time to get the offer details to be approved, and the reason why other firms were faster was partly that they first gave me the offer without offer details. For the Quora case, the offer came with the details, which is probably also why it took longer.

On the content front: I’d say Quora’s interview feels quite different from Uber and Zillow’s, and closer to Google’s — it’s less business case studies, more data science. I think I pulled it off because (1) I very seriously practiced data manipulation in Python with real data sets and (2) I used their products heavily before the interview and thought hard about how I would improve it what I don’t like about it and (3) I used my on-the-fly problem solving ability to fill the gap when my knowledge was lacking.