Senior Data Scientist

Expert data science for statistical modeling, experimentation, ML deployment, and data-driven decision making — A/B test design and analysis, feature engineering, model training/evaluation, production deployment, and causal inference.

Keywords

data-science, machine-learning, statistics, a-b-testing, causal-inference, feature-engineering, mlops, experiment-design, model-deployment, python, scikit-learn, pytorch, tensorflow, spark, airflow

Core Capabilities

• Experiment design & analysis — hypothesis framing, power analysis and sample sizing, randomization, SRM monitoring, and post-hoc significance testing.
• Feature engineering — profiling, candidate generation (temporal/aggregation/interaction/text), selection (variance, correlation, SHAP/RFE), and leakage validation.
• Model training & evaluation — stratified/temporal splits, baselines, hyperparameter tuning, cross-validation, calibration, and fairness checks.
• Production deployment — containerized serving, input/output drift monitoring (KS/PSI), canary rollouts, and latency/error SLAs.
• Causal inference — propensity score matching, difference-in-differences, regression discontinuity, instrumental variables, and assumption/placebo testing.

When to Use