As a product-minded Lead Data Scientist, I help organizations move from ambiguity to impact by building and scaling data products that drive intelligent decisions, engaging user experiences, and measurable growth. With 9+ years of experience across analytics, experimentation, data-driven decision systems, and applied AI/ML, I have led initiatives that translate complex data into systems that influence behavior, improve operational efficiency, and create long-term strategic advantage. My work spans machine learning, uplift modeling, and causal frameworks that guide business decisions, as well as AI agents, RAG-based LLMs, automated data pipelines, enterprise dashboards and analytics platforms that accelerate knowledge access, KPI visibility, and execution velocity. I specialize in taking AI and data solutions from 0 to 1 and scaling them to enterprise adoption, collaborating closely with product managers, engineers, and cross-functional stakeholders to ensure solutions are scalable, interpretable, and aligned with product vision. I believe effective data science requires more than technical depth. It takes systems thinking, ethical design, and the ability to connect technology with user value. That is the perspective I bring to every product and problem I work on. Focus Areas: • AI Product Strategy • A/B Testing and Causal Inference • Analytics and Dashboards • GenAI Systems and AI Agent Design • Data Infrastructure and ML Deployment • Experimentation Platforms Tech Stack: • Analytics and Modeling: SQL, Python, PySpark, Snowflake, Tableau, A/B Testing, ETL Pipelines, CI/CD, XGBoost, MLflow, Causal Inference (PSM, DiD, IPTW, Meta Learners) • MLOps and Deployment: AWS, Azure, Airflow, Spark, Dask, Git, Docker, Kubernetes, Hadoop, FastAPI, NVIDIA GPUs • Generative AI: AI Agents, Model Context Protocol (MCP), LangChain, Retrieval Augmented Generation (RAG), RAGAS, LoRaX Fine Tuning, Transformers, Vector Databases, Embeddings, LLMOps Let’s connect to exchange ideas on data science, experimentation design, and GenAI.