Sr. Data Scientist

We are seeking a Sr. Data Scientist to serve as a senior owner for production data science outcomes, combining advanced modeling, experimentation, optimization, and stakeholder leadership to deliver measurable value across business processes. This role is accountable for senior independent model ownership and cross-functional influence and is expected to operate at the level of 4–8 production models, decision engines, experiments, or GenAI workflows across one or more domains, with influences experimentation cost, model operating cost, and build-versus-buy recommendations for owned work.

The role differentiates Data Science ownership of problem framing, model behavior, experimentation, value measurement, adoption, and production model health from AI Engineering ownership of scalable platform foundations. The candidate will partner closely with domain leaders, product owners, AI engineers, data engineers, and senior business stakeholders to convert analytical rigor into decisions, workflow change, and measurable performance improvement.

• Problem Framing & Value: Own problem framing for 4–8 production models, decision engines, experiments, or GenAI workflows across one or more domains by quantifying baselines, decision points, adoption paths, and expected value before modeling begins, with outcomes tied to multi-process improvements in revenue, cost, service, capacity, personalization, or operational decision quality.
• Predictive Modeling: Develop and validate high-performing predictive models using Python, scikit-learn, XGBoost, Light
  
  GBM, Cat Boost, Databricks, feature stores, and robust backtesting appropriate to production decisioning.
• Prescriptive Decisioning: Design optimization, recommendation, simulation, or scenario-planning engines that translate predictions into actions, constraints, tradeoffs, and measurable operational or commercial lift.
• GenAI Solutions: Build GenAI use cases with GPT-class models, Azure AI Foundry, RAG, embeddings, prompt libraries, evaluation harnesses, and safety tests, focusing on business process improvement rather than novelty.
• Statistical Experimentation: Lead experimentation strategy using A/B tests, causal inference, quasi-experimental designs, bootstrap methods, and sensitivity analysis to prove whether interventions drive incremental value.
• Explainability & Trust: Create trust mechanisms using SHAP, counterfactual analysis, model cards, residual/error analysis, human review loops, and stakeholder-ready narratives that expose limitations and decision implications.
• Production Deployment: Partner with AI Engineering to product ionize models through Databricks, Azure ML, MLflow, APIs, batch scoring, or containerized services while maintaining ownership of model quality, value, and adoption.
• Production Operations: Own post-launch model health by monitoring accuracy, drift, calibration, bias, adoption, financial KPIs, latency, and cost, then driving retraining, rollback, or operating-process changes when needed.
• Stakeholder Partnership: Lead cross-functional adoption with business, product, operations, AI Engineering, and data engineering teams so model outputs become decisions, workflow changes, and measurable performance improvements.

• Education: Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Operations Research, Engineering, Economics, or related quantitative field, or equivalent experience delivering production models at comparable scale.
• Experience: Proven experience at senior scope delivering 4–8 production models, decision engines, experiments, or GenAI workflows across one or more domains, including production use, stakeholder adoption, value tracking, model operations, and measurable improvement in business outcomes.
• ML Tooling: Advanced experience with Python, scikit-learn, XGBoost, Light
  
  GBM, Cat Boost, PyTorch/Tensor Flow where relevant, model evaluation, hyperparameter tuning, backtesting, and feature engineering.
• Optimization: Strong experience applying MILP, simulation, dynamic programming, heuristics, stochastic methods, or prescriptive analytics to constrained,…