Define and prioritize an analytics, ML, and AI engineering roadmap; translate business strategy into a portfolio of models, experiments, LLM applications, and analytical products with clear success metrics.

Lead end-to-end delivery of complex ML, statistical, and generative AI solutions—from problem framing and data strategy through deployment, monitoring, evaluation, and iteration—in partnership with MLOps and engineering.

Build and evolve an extensible data science and AI platform on Databricks (Python, SQL, Databricks ML, Model Serving, Asset Bundles) integrated with Azure OpenAI and related services.

Design and ship applied AI systems relevant to IMA work: prompt and context engineering, LLM orchestration pipelines (e.g. KPI → narrative → media/HTML), retrieval-augmented generation (RAG) where appropriate, evaluation harnesses, guardrails, and Copilot/agent workflows that automate operational processes.

Own architecture and standards for forecasting, recommender/personalization systems, predictive analytics, and production AI apps; ensure reproducibility, documentation, CI/CD readiness, and operational excellence.

Identify revenue and efficiency opportunities using advanced analytics and AI; initiate projects that bridge operational metrics to financial and customer outcomes.

Provide a feature store, reusable components, and technical support that accelerate other analytics teams.

Mentor and coach data scientists and analysts through design reviews, code reviews, and pairing—raising the bar on experimentation, responsible AI, and production quality—without formal supervisory responsibility.

Respond quickly to high-impact ad-hoc requests; deliver actionable insights and clear recommendations to leadership.

Perform prediction modeling, customer lifetime value, and related analyses that inform strategy.

Ensure MLOps / AIOps discipline: maintain production code and assets, monitoring, retraining/refresh patterns, incident response, and reliability for models and generative pipelines.

Represent IMA analytics with senior business and technology stakeholders: set expectations, manage trade-offs, and communicate implications for operations and financial outcomes.

Participate in data governance—metrics definitions, data quality, and source improvement—and formulate UX strategies so analytical and AI outputs are adopted by end users.

Perform additional project-based and ad-hoc analysis as assigned.