DR. PRIYA SHARMA Senior Data Scientist I build statistical models that change how organizations make decisions. PhD in computational neuroscience, six years in industry turning messy data into defensible conclusions. priya@priyasharma.io · +1 (617) 555 0293 · Boston, MA · priyasharma.io · Google Scholar: https://scholar.google.com/citations?user=priyasharma · GitHub: https://github.com/priyasharma · LinkedIn: https://linkedin.com/in/priyasharma ABOUT ───────────── I spent four years in a neuroscience lab studying how the brain encodes uncertainty, then realized the same math applies to every business problem I've seen since. At Moderna I built the forecasting models that guided $200M in manufacturing capacity planning. At Two Sigma I developed causal inference pipelines that reshaped how portfolio managers think about factor attribution. I care about reproducibility, honest uncertainty quantification, and making sure the person reading my analysis can actually act on it. I publish in peer-reviewed venues and ship production ML systems — I don't think those should be separate careers. EXPERIENCE ────────────────── Senior Data Scientist, Supply Chain Intelligence — Moderna · Cambridge, MA 2023 – Present Lead data scientist for demand forecasting and manufacturing optimization across the mRNA vaccine portfolio. • Built hierarchical Bayesian demand model covering 14 markets; reduced forecast MAPE from 32% to 11%, directly informing $200M+ in capacity investment. • Designed causal impact framework for evaluating process changes on yield; identified two interventions worth $18M/year in recovered product. • Created an internal Python library (moderna-ts) for time-series preprocessing now used by 40+ analysts across three business units. • Mentored two junior scientists through their first production model deployments. Python · PyMC · Stan · Snowflake · Airflow · Docker Data Scientist, Factor Research — Two Sigma · New York, NY 2020 – 2023 Quantitative research on causal factor models for systematic equity strategies. • Developed a double-ML causal attribution pipeline that decomposed portfolio returns into 23 interpretable factors with confidence intervals; adopted firm-wide. • Built real-time anomaly detection for data vendor feeds using conformal prediction; caught 14 data quality incidents before they reached production. • Published internal research note on synthetic control methods for strategy evaluation, later adapted into a NeurIPS workshop paper. Python · R · Spark · Kubernetes · PostgreSQL Postdoctoral Researcher — MIT — McGovern Institute · Cambridge, MA 2018 – 2020 Computational neuroscience research on probabilistic models of sensory decision-making. • First-authored paper in Nature Neuroscience on Bayesian models of multi-sensory integration in the primate cortex (87 citations). • Built an open-source Julia package (BayesBrain.jl) for neural population decoding; 600+ GitHub stars. • Co-taught MIT 9.40 (Introduction to Neural Computation) for two semesters. Julia · Python · MATLAB · Stan · SLURM SELECTED PUBLICATIONS ───────────────────────────── Sharma P, Chen L, Bhatt R. "Bayesian multi-sensory integration in primate parietal cortex". Nature Neuroscience (2019) Sharma P, Goldstein A. "Causal factor attribution with double machine learning: a practitioner's guide". NeurIPS Workshop on Causal Inference (2022) Sharma P, Liu W, Park J. "Conformal anomaly detection for streaming financial data". ICML (2023) Sharma P, Rodriguez M. "Hierarchical demand forecasting for pharmaceutical manufacturing". arXiv preprint (under review at Management Science) (2025) OPEN-SOURCE PROJECTS ──────────────────────────── BayesBrain.jl — Creator (2019–) https://github.com/priyasharma/BayesBrain.jl Julia package for Bayesian neural population decoding. Used in 8 published papers by 4 different labs. Tech: Julia, Stan causal-kit — Creator (2022–) https://github.com/priyasharma/causal-kit Python library for causal inference in observational studies. Implements DML, synthetic controls, and IV regression with scikit-learn-compatible API. Tech: Python, scikit-learn, PyTorch conformal-stream — Creator (2023) https://github.com/priyasharma/conformal-stream Streaming conformal prediction with adaptive calibration. Companion code for ICML 2023 paper. Tech: Python, NumPy TECHNICAL SKILLS ──────────────────────── Statistical Methods: Bayesian inference, Causal inference, Time series, Survival analysis, Experimental design ML & Deep Learning: PyTorch, scikit-learn, XGBoost, Transformers, Conformal prediction Languages: Python, R, Julia, SQL, Stan Infrastructure: Airflow, Spark, Docker, Kubernetes, AWS, Snowflake EDUCATION ───────────────── PhD, Computational Neuroscience — Stanford University (2013 – 2018) NSF Graduate Research Fellowship. Dissertation: "Bayesian computation in neural circuits for multisensory decision-making." BTech, Electrical Engineering — Indian Institute of Technology, Delhi (2009 – 2013) President's Gold Medal (highest GPA in cohort). Minor in Mathematics. AWARDS & HONORS ─────────────────────── NSF Graduate Research Fellowship — National Science Foundation (2014) Best Paper, NeurIPS Causal Inference Workshop — NeurIPS (2022) President's Gold Medal — IIT Delhi (2013) CERTIFICATIONS ────────────────────── AWS Certified Machine Learning — Specialty — Amazon Web Services (2024) LANGUAGES ───────────────── English — Native · Hindi — Native · French — Intermediate COMMUNITY ───────────────── Mentor — Data Science for Social Good 2021 – Present Summer fellowship mentor; guided three teams building ML models for non-profits in public health and criminal justice reform.