I'm a Senior Data Scientist and Tech Lead focused on applied AI, scalable data systems, and product-led delivery. I combine technical depth with decisive leadership to turn complex data problems into measurable business outcomes across legal, healthcare, education, energy and agriculture.

I lead cross-functional teams, define technical and product vision, and deliver end-to-end solutions — from data architecture and model lifecycle automation to operational integration and stakeholder alignment. My approach is results-first: clear goals, fast iterations, repeatable delivery.

• AI & ML: LLMs, Retrieval-Augmented Generation (RAG), supervised learning, model evaluation & monitoring
• Data & Engineering: Databricks, Spark, MLflow, Airflow, data architecture, APIs, scalable pipelines
• Cloud & Ops: Serverless and event-driven patterns on AWS (Lambda, SQS, Batch, Bedrock), CI/CD for models
• Product & Strategy: Roadmaps, OKRs, product metrics, prioritization, user-centered ML features
• Leadership & Delivery: Team hiring & mentoring, stakeholder management, cross-functional alignment, technical vision, cost & risk trade-offs
• Business Impact: Translating technical work into revenue/efficiency outcomes, operational KPIs, and improved decision latency

• Designed Generative AI solutions that automated document interpretation and drastically reduced manual triage time.
• Built recommendation systems and personalization layers to improve user engagement and content relevance.
• Implemented serverless ingestion and processing pipelines to accelerate research and data workflows.
• Delivered AI-driven automation for O&M processes, improving reliability and lowering operational costs.
• Led predictive modeling and decision-support solutions for digital agriculture, enhancing operational performance.

• Goal-oriented: define measurable outcomes and iterate quickly.
• Hands-on leader: write code, review architecture, and unblock teams.
• Pragmatic trade-offs: balance speed, cost, maintainability and model quality.
• Communicative: translate technical choices into business implications for execs and product teams.