Andrew Rothman
Staff Applied Scientist (Machine Learning & Causal Inference) (E-commerce) @The Cambridge Group
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WORK HISTORY
Staff Applied Scientist (Machine Learning & Causal Inference) (E-commerce) @The Cambridge Group
Staff Applied Scientist leading design and execution of end-to-end Deep Learning and Causal Inference projects (from inception to production) for Fortune 100 technology, retail, and e-commerce clients. Projects have included Deep Learning driven revenue forecasting, customer segmentation, and recommender systems leveraging unstructured natural language data and e-commerce click-stream data. Methodologies include custom stacked ensemble SuperLearners deployed end-to-end on AWS.
EDUCATION
Harvard University
Master of Science (Sc.M.), Biostatistics & Epidemiology, (Statistics)
Stanford University
Non-Degree Graduate Student - Computer Science, Artificial Intelligence, & Machine Learning
Harvard University
Doctor of Science (Sc.D.) Candidate, Biostatistics & Epidemiology, (Statistics)
Massachusetts Institute of Technology
Cross-Registered Harvard University Student, Computer Science, Statistics
Tecnológico de Monterrey
Study Abroad (January 2007 - June 2007)
Boston University
Bachelor of Science (B.S.), Biomedical / Electrical Engineering
SKILLS
ABOUT ANDREW ROTHMAN
BACKGROUND- Harvard & MIT trained Statistician and Machine Learning (ML) Scientist with 10+ years experience- Currently Principal Applied Data Scientist at The Cambridge Group (TCG) leading end-to-end (inception to production) DL and Causal Inference projects for Fortune 100 clients in tech, retail, and e-commerce- Teaching @ Stanford including XCS234: Reinforcement Learning, XCS224W: ML over Graphs & Networks, and XCS221: Artificial Intelligence- Former Chief Data Scientist in the Finance and Venture Capital/Private Equity spaces (AIMatters & BuildGroup), leading design and building of Deep Learning, Natural Language Processing (NLP), and Reinforcement Learning systems for identification of investment opportunities & ML-built ETF (Exchange Traded Fund)- Former Research Fellow at Harvard University, and former Adjunct Faculty in Statistics at MCPHS University- Trained in state-of-the-art Causal Inference techniques by pioneering Harvard faculty (https://causalab.sph.harvard.edu/), with expertise in G-Methods (i.e. G-formula, G-Estimation, etc), Doubly-Robust Estimation, Causal Discovery, Targeted Maximum Likelihood Estimation (TMLE), Double Machine Learning- Open to top-tier Applied Scientist/Quantitative Researcher roles: a••••••••@gmail.comMACHINE LEARNING / DEEP LEARNING EXPERTISE- Deep Learning (ConvNet, RNN, LSTM, Transformer, etc)- “Traditional” Machine Learning (Random Forests, Gradient Boosting, SVMs, Stacked Ensembles, etc)- Natural Language Processing (NLP)- Computer Vision- Reinforcement Learning- Generative Learning - Probabilistic Graphical Models- Graphical/Network Machine Learning- Recommender Systems- Interpretable AI- ML + Causal Inference (TMLE, Double Machine Learning)STATISTICS EXPERTISE- Mathematical Statistics- Stochastic Processes- Statistical Learning Theory- Bayesian Inference (Parametric & Nonparametric)- Survival Methods- Advanced Study Designs (Observation, Case-Control, etc)- Experimentation (A/B testing, Multi-Armed Bandits, Adaptive Trial Design, etc)- Causal Inference Methods (G-methods, Propensity Score methods, IV estimators, etc)SOFTWARE/PROGRAMMING STACK- Scientific Computing: Python (NumPy, Pandas, Scikit-learn, Matplotlib, etc), R, C++, MATLAB, STATA, SAS- DL frameworks & Optimizers: PyTorch, TensorFlow, Optuna, Hyperopt- Distributed Computing: PySpark, MLlib (exposure to native Spark w/ Scala, & Hadoop)- SQL: MySQL, Microsoft SQL Server- Production: Docker, Flask, Airflow, MLflow, Git, CircleCI- Cloud: Amazon AWS (exposure to Microsoft Azure and Google GCP)
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