I build production machine learning and AI systems that operate at the scale of Azure — forecasting demand, guiding multi‑billion‑dollar capacity decisions, and bringing generative AI into how a global cloud business plans and operates.
As a Senior Data & Applied Scientist at Microsoft's Capacity Planning (MCP) Centre of Excellence, I own forecasting and analytical systems that anticipate cloud demand, optimize resource allocation, and reduce operational risk across Azure. My work spans the full stack of applied ML — machine learning, deep learning, statistical modeling, and large‑scale data analysis — shipped as production systems, not prototypes.
Most recently, I've been architecting LLM‑powered agents and retrieval‑grounded systems that turn complex business data into executive‑ready insight — cutting time‑to‑decision from hours to seconds. While capacity forecasting is my current focus, I work across the breadth of machine learning and generative AI, and I'm equally at home framing an ambiguous problem, designing the model, and deploying it.
Beyond my core role, I'm committed to growing the next generation of data talent — I mentor aspiring data scientists, teach at leading ed‑tech platforms, contribute to academic curricula and research, and deliver keynotes and workshops on real‑world AI, cloud, and analytics.
I thrive at the intersection of engineering, business, and research — using data to solve hard problems, drive strategic decisions, and create meaningful impact.
