For the given job description <<This is a 3+ month contract and hybrid to Riverwoods, Il 
Must HaveAd Hoc AnalysisFinancial AccountingMicrosoft ExcelOracle
JOB DESCRIPTIONReports to director for accounting processes in Controller's department.Role will engage with business partners on processes on behalf of (and under direction of) director. The processes include the advancement of initiatives and projects, with decision-making to be guided by director. Knowledge of functions being supported will be obtained through interaction with existing team members.Advance various preventative risk activities and initiatives with moderate supervision.Assist staff in completion of daily general ledger account reconciliation activities and end-user-computing review. Assist director in management and escalation of activities impacting reconciliation processes performed by the team.Assist in the execution of reporting and analysis performed by the team, including that relating to month-end close and ad hoc reporting. Also may include assessing the reasonableness of accounting-related materials that support accruals and impairment analyses.Reflect an understanding of accounting principles, strong communication (verbally and written), and sound decision-making>> the resume: <<Summary12+ years of machine learning/data science experience with a proven track record of developing and
       implementing large scale algorithms that have significantly impacted business revenues and user experience.
       10+ years of experience applying R, Python, SAS and SQL for algorithm development, data modeling, statistical
       learnings and data visualization.
       Ranked a top 1% competitor on Kaggle (worlds largest community of data scientist with over 300k members)
       Hands-on experience applying several ML/Statistical algorithms to real-world problems: Neural Networks,
       Gradient Boosted Trees, Random Forest, Clustering, Generalized Linear Models, Simulation models and
       Gaussian Mixture Models
       Strong experience building end-to-end machine learning platform using Java and big data technologies
       (Cassandra, Spark and Hive).
       Excellent skills at programming in Java/OOP
HighlightsR          Neural NetworksJava          Gradient Boosted TreesPython          Random ForestNoSQL, Cassandra          Generalized Linear ModelsSpark, Hive, Pig          OptimizationSQL, CQL          Gaussian Mixture Models
Experience04/2017toCurrentSenior Data ScientistFactset Research Systems Inc.–Reston,VA,Conceptualized, architected and implemented a deep learning model to accurately predict demand for Nvidia's products. Algorithm applies Deep neural networks along with several real time social feeds, econometric indicators, product momentum to forecast demand.06/2014to04/2016Data Science LeadBristol Myers Squibb–Minneapolis,MN,Developed and owned the core machine learning feature of the product, a patent-pending behavioral analytics engine for
predicting cross-cloud application security threats.Architected and implemented the machine learning platform from the
ground up in Java, Cassandra, Hive and Spark.Analytic platform monitors multiple cloud applications (AWS, Salesforce,
Box etc.) and predicts security threats and breaches.Implemented the end-to-end platform for performing user behavior analytics using unsupervised machine
        learning.Designed and implemented algorithms for real-time decisioning
        Implemented process to capture 500+ behavioral profile features
        Implemented several high IV features for ML algorithm consumption: (Maximum Geo-distance between users
        login/day, Fast Geolocation Hopping etc.)
        Owned the feature end-to-end and worked directly with co-founder/CTO to drive product enhancements.12/2012to05/2014Principal Data ScientistLumeris–Saint Louis,MO,Lead several big data machine learning initiatives involving the design, development and deployment of advanced
machine learning algorithms that impacted PayPal's core products allowing business to grow and scale to over 150
million customers and process over 3 billion online transactions annually.The developed solutions have directly helped
PayPal lower losses, improve user experience and increase revenue simultaneously.Few key PayPal core products
worked on are included below.PayPal ATO Models:
        Designed and developed Neural Network models to prevent account takeover activity at the time of user
        authentication.Performed feature selection from over 2000 features, and applied best practices in Neural Network
        model development.Model accurately captured 81% of incoming fraud activity and resulted in 48bps of reduction
        of losses for PayPal in 2013 freeing up capital to grow and expand margins.PayPal Here Mobile Card Reader:
        Architected the very first anti-fraud ML algorithm that enabled PayPal here product to expand and scale to over
      million merchants.Designed many new geolocation specific features that showed high IV contribution to the
      implemented model.PayPal Wallet:
      Spearheaded the development of ensemble models for predicting stolen financial usage at the time of transaction
      completion.Developed Gradient Boosted Trees using GBM package in R.Implemented new features using IP,
      IP-GEO, Velocity etc.to improve algorithm performance.Designed several tools in R to visualize GBT model and
      developed process to extract model parameters for deployment.Partnered with business and product teams to
      execute and influence business decision around the solution implementation.05/2007to11/2012Lead Analytic ScientistCushman & Wakefield Inc–Stockton,CA,Managed the design, development and deployment of several predictive modeling solutions that underlie ISO Risk
Analyzer portfolio of products which have contributed to significant revenues for the company.Few highlights are listed
below.Lead the development of an expert system to accurately predict a consumer's likelihood of committing a traffic violation.Developed Logistic models for predicting violation probability and Poisson models for predict violation frequency.Incorporated intelligence from 1500 features from 7 different data sources (Claritas, Crime, Traffic Patterns, Business Info, Weather etc.) Tweedie/Compound Poisson Distribution Model Developed and implemented several Tweedie distribution models to examine environmental factor for predicting personal auto risks for calculating insurance premiums.Developed separate frequency and severity models to improve model accuracy.Captured interaction effects between various environment variables such as traffic generator, traffic pattens, population density, weather etc.Risk Analyzer Credit Scoring Module: Designed and developed first generation credit scoring algorithms to predict a consumer's accident likelihood using credit payment behavior.Performed feature reduction on close to 800 credit attributes and applied Multivariate Adaptive Regression Splines (MARS) to detect 2nd and 3rd order interactions effects.Captured non-linearity using splines and piecewise regression.Worked as the analytic manager for the movie advertisement testing product, a high revenue growth initiative for the company.09/2004to04/2007Senior Research AnalystNbc Universal–Boston,MA,Managed 5 statisticians to build predictive models for rating movie trailer/TV spots content on their
effectiveness to generate interest.Provided analytic insights and advice to maximize the impact and reach of client's
resources.The product served 4 of the top hollywood movie studios and generated over $13M in annualized revenues for
the company.Movie Trailer/TV Spots Advertisement Testing Product (Predictive models for rating movie trailers, TV spots, print ads
etc.).Team worked on over 100 movies for most of the motion picture studios-majors, and assisted in building
        customized statistical models for several mini-majors.Provided strategic insights and advice to Hollywood
        Studios to maximize the impact and reach of clients marketing resources.Box Office Forecasting Models:
        Developed forecasting models to predict weekend box office revenues to help studios estimate accurately a films
        potential at the day of the release.Model used several audience tracking data to accurately predict weekend box
        office receipts.Fox Movie Sequel Study (Client - 20th Century Fox) ?
        Developed a series of ordinal logistic regression models to compare characteristics and attributes describing
        several Fox movies.Based on the study results, 20th Century Fox decided to produce a sequel to Die Hard in
        2007, which was one of the biggest box office hits of that year.01/2002to01/2004ResearcherUniversity Of Missouri–City,STATE,Managed home healthcare initiative for Missouri's Sinclair School that involved developing a AI system based GPS to
optimize hospital resources for saving cost.The GPS system incorporated new algorithms for solving multiple-traveling
salesman problem in a dynamic setting.Researched and applied spatial clustering algorithms in the context of
constraint optimization problem.
EducationExpected in2004totoMaster of Science:Industrial Engineering/Operations ResearchUniversity of Missouri-Columbia,MOGPA:Industrial Engineering/Operations Research Thesis - "Dynamic Stochastic Vehicle Routing Model in Home Healthcare Scheduling"Expected in2002toto:Metallurgical EngineeringIndian Institute of Technology-,GPA:Metallurgical Engineering BHU          Varanasi, UP, IN
Thesis - "Monte Carlo Simulation Modeling of Non-Uniform Stress in Structured Materials"
{Secured All India IITJEE rank in the top 0.6% -- ranked among 250,000 applicants}
Skillsads, AI, big data, clustering, content, Credit, Client, clients, expert system, Fast, features, financial, Forecasting, GPS, Home Healthcare, insurance, IP, ISO, Java, machine learning, machine
        learning, marketing, MARS, Materials, Office, 2000, Modeling, monitors, Network, Networks, Neural, NoSQL, Optimization, predict, Testing Product, Python, real-time, Routing, Scheduling, Simulation, SQL, strategic, Structured, TV>>. The result is, No Fit

Fit assessment: No Fit