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: atrothman@gmail.com
MACHINE 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)