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

• Designing or analyzing an A/B test.
• Building a feature engineering pipeline.
• Training, evaluating, or deploying an ML model.
• Estimating treatment effects from observational data.

Clarify First

Before running an analysis or pipeline, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Task — A/B test design / feature engineering / model evaluation / causal inference (selects the script and workflow)
• Dataset & target variable — what you are modeling or measuring (drives feature generation and leakage validation)
• Decision metric & minimum effect — the metric and the smallest effect worth detecting (drives power analysis and sample size)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

Script	Purpose
scripts/experiment_designer.py	A/B test design, power analysis, sample size calculation
scripts/feature_engineering_pipeline.py	Automated feature generation, correlation analysis, feature selection
scripts/statistical_analyzer.py	Hypothesis testing, causal inference, regression analysis
scripts/model_evaluation_suite.py	Model comparison, cross-validation, deployment readiness checks

statistical_analyzer.py is referenced but not yet present in the repo — see the note in references/ds-operations.md. Use inline scipy/statsmodels in the meantime.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/ds-workflows.md — quick-start commands, tech stack, the five end-to-end workflows (A/B testing, feature pipeline, train/evaluate, deploy, causal inference) with Python snippets, performance targets, and common commands. Read when executing any data-science task.
• references/ds-operations.md — troubleshooting table, success criteria, and the full CLI flag reference for each script. Read when diagnosing issues or running the tools.
• references/statistical_methods_advanced.md — advanced statistical methods reference (hypothesis testing, causal inference, regression). Read for statistical depth.
• references/experiment_design_frameworks.md — experiment design frameworks and power-analysis foundations. Read when designing rigorous experiments.
• references/feature_engineering_patterns.md — feature engineering patterns and selection techniques. Read when building features.

Scope & Limitations

This skill covers:

• End-to-end experiment design including power analysis, randomization, and post-hoc analysis
• Feature engineering pipelines with profiling, generation, selection, and validation
• Model training evaluation including cross-validation, calibration, and fairness checks
• Production model deployment with monitoring, drift detection, and canary rollouts

This skill does NOT cover:

• Data engineering infrastructure (ETL orchestration, pipeline scheduling, data lake management) -- see senior-data-engineer
• Deep learning model architecture design and training at scale (distributed GPU training, custom layers) -- see senior-ml-engineer
• Prompt engineering, RAG systems, and LLM fine-tuning workflows -- see senior-prompt-engineer
• Computer vision pipelines (object detection, segmentation, video processing) -- see senior-computer-vision

Integration Points

Skill	Integration	Data Flow
senior-data-engineer	Feature pipeline ingests data from ETL outputs; shares data quality validation patterns	Raw data stores --> feature engineering pipeline --> feature store
senior-ml-engineer	Trained models handed off for MLOps deployment; shares model registry and serving configs	Evaluated model artifacts --> deployment pipeline --> production serving
senior-prompt-engineer	Embedding features from LLMs feed into ML pipelines; experiment frameworks apply to prompt A/B tests	LLM embeddings --> feature vectors; experiment designs --> prompt evaluation
senior-architect	Model serving architecture reviewed for scalability; data platform design aligned with training infrastructure	Architecture specs --> deployment topology --> monitoring dashboards
senior-backend	Model inference endpoints integrated into backend services; API contracts defined for prediction requests	REST/gRPC model API --> backend service layer --> client applications
senior-devops	CI/CD pipelines extended for model retraining triggers; containerized model images deployed via infrastructure-as-code	Docker images --> Kubernetes manifests --> production clusters