senior-data-scientist

What this skill does

# Senior Data Scientist

Expert data science for statistical modeling, experimentation, ML deployment,
and data-driven decision making.

## 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

---

## Quick Start

```bash
# Design an experiment with power analysis
python scripts/experiment_designer.py --input data/ --output results/

# Run feature engineering pipeline
python scripts/feature_engineering_pipeline.py --target project/ --analyze

# Evaluate model performance
python scripts/model_evaluation_suite.py --config config.yaml --deploy

# Statistical analysis
python scripts/statistical_analyzer.py --data input.csv --test ttest --output report.json
```

---

## 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 |

---

## Tech Stack

| Category | Tools |
|----------|-------|
| Languages | Python, SQL, R, Scala |
| ML Frameworks | PyTorch, TensorFlow, Scikit-learn, XGBoost |
| Data Processing | Spark, Airflow, dbt, Kafka, Databricks |
| Deployment | Docker, Kubernetes, AWS SageMaker, GCP Vertex AI |
| Experiment Tracking | MLflow, Weights & Biases |
| Databases | PostgreSQL, BigQuery, Snowflake, Pinecone |

---

## Workflow 1: Design and Analyze an A/B Test

1. **Define hypothesis** -- State the null and alternative hypotheses. Identify the primary metric (e.g., conversion rate, revenue per user).
2. **Calculate sample size** -- `python scripts/experiment_designer.py --input data/ --output results/`
   - Specify minimum detectable effect (MDE), significance level (alpha=0.05), and power (0.80).
   - Example: For baseline conversion 5%, MDE 10% relative lift, need ~31,000 users per variant.
3. **Randomize assignment** -- Use hash-based assignment on user ID for deterministic, reproducible splits.
4. **Run experiment** -- Monitor for sample ratio mismatch (SRM) daily. Flag if observed ratio deviates >1% from expected.
5. **Analyze results:**
   ```python
   from scipy import stats

   # Two-proportion z-test for conversion rates
   control_conv = control_successes / control_total
   treatment_conv = treatment_successes / treatment_total
   z_stat, p_value = stats.proportions_ztest(
       [treatment_successes, control_successes],
       [treatment_total, control_total],
       alternative='two-sided'
   )
   # Reject H0 if p_value < 0.05
   ```
6. **Validate** -- Check for novelty effects, Simpson's paradox across segments, and pre-experiment balance on covariates.

## Workflow 2: Build a Feature Engineering Pipeline

1. **Profile raw data** -- `python scripts/feature_engineering_pipeline.py --target project/ --analyze`
   - Identify null rates, cardinality, distributions, and data types.
2. **Generate candidate features:**
   - Temporal: day-of-week, hour, recency, frequency, monetary (RFM)
   - Aggregation: rolling means/sums over 7d/30d/90d windows
   - Interaction: ratio features, polynomial combinations
   - Text: TF-IDF, embedding vectors
3. **Select features** -- Remove features with >95% null rate, near-zero variance, or >0.95 pairwise correlation. Use recursive feature elimination or SHAP importance.
4. **Validate** -- Confirm no target leakage (no features derived from post-outcome data). Check train/test distribution alignment.
5. **Register** -- Store features in feature store with versioning and lineage metadata.

## Workflow 3: Train and Evaluate a Model

1. **Split data** -- Stratified train/validation/test split (70/15/15). For time series, use temporal split (no future leakage).
2. **Train baseline** -- Start with a simple model (logistic regression, gradient boosted trees) to establish a benchmark.
3. **Tune hyperparameters** -- Use Optuna or cross-validated grid search. Log all runs to MLflow.
4. **Evaluate on held-out test set:**
   ```python
   from sklearn.metrics import classification_report, roc_auc_score

   y_pred = model.predict(X_test)
   y_prob = model.predict_proba(X_test)[:, 1]

   print(classification_report(y_test, y_pred))
   print(f"AUC-ROC: {roc_auc_score(y_test, y_prob):.4f}")
   ```
5. **Validate** -- Check calibration (predicted probabilities match observed rates). Evaluate fairness metrics across protected groups. Confirm no overfitting (train vs test gap <5%).

## Workflow 4: Deploy a Model to Production

1. **Containerize** -- Package model with inference dependencies in Docker:
   ```bash
   docker build -t model-service:v1 .
   ```
2. **Set up serving** -- Deploy behind a REST API with health check, input validation, and structured error responses.
3. **Configure monitoring:**
   - Input drift: compare incoming feature distributions to training baseline (KS test, PSI)
   - Output drift: monitor prediction distribution shifts
   - Performance: track latency P50/P95/P99 targets (<50ms / <100ms / <200ms)
4. **Enable canary deployment** -- Route 5% traffic to new model, compare metrics against baseline for 24-48 hours.
5. **Validate** -- `python scripts/model_evaluation_suite.py --config config.yaml --deploy` confirms serving latency, error rate <0.1%, and model outputs match offline evaluation.

## Workflow 5: Perform Causal Inference

1. **Assess assignment mechanism** -- Determine if treatment was randomized (use experiment analysis) or observational (use causal methods below).
2. **Select method** based on data structure:
   - **Propensity Score Matching**: when treatment is binary, many covariates available
   - **Difference-in-Differences**: when pre/post data available for treatment and control groups
   - **Regression Discontinuity**: when treatment assigned by threshold on running variable
   - **Instrumental Variables**: when unobserved confounding present but valid instrument exists
3. **Check assumptions** -- Parallel trends (DiD), overlap/positivity (PSM), continuity (RDD).
4. **Estimate treatment effect** and compute confidence intervals.
5. **Validate** -- Run placebo tests (apply method to pre-treatment period, expect null effect). Sensitivity analysis for unobserved confounding.

---

## Performance Targets

| Metric | Target |
|--------|--------|
| P50 latency | < 50ms |
| P95 latency | < 100ms |
| P99 latency | < 200ms |
| Throughput | > 1,000 req/s |
| Availability | 99.9% |
| Error rate | < 0.1% |

---

## Common Commands

```bash
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/

# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth

# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/

# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py
```

---

## Reference Documentation

| Document | Path |
|----------|------|
| Statistical Methods | [references/statistical_methods_advanced.md](references/statistical_methods_advanced.md) |
| Experiment Design Frameworks | [references/experiment_design_frameworks.md](references/experiment_design_frameworks.md) |
| Feature Engineering Patterns | [references/feature_engineering_patterns.md](references/feature_engineering_patterns.md) |
| Automation Scripts | `scripts/` directory |

---

## Troubleshooting

| Problem | Cause | Solution |
|---------|-------|----------|
| Sample size calculation returns unreasonably large numbers | Minimum detectable effect (MDE) is set too small relative to baseline variance | Increase MDE to a practically meaningful threshold or accept longer experiment duration |
| Feature pipeline reports high null rates acro