I build practical product systems that turn data, analytics, and agentic AI into clearer decisions and faster action.
I work at the intersection of strategy, analytics, product discovery, and information systems. My bias is toward practical tools that reduce manual judgment, clarify decisions, and help teams act faster.
The pattern: find the unclear decision, structure the data around it, and ship something usable enough to change how people work.
Problem: Teams relied on manual, bias-prone methods to identify influential doctors.
Role: Integrated datasets, configured scoring logic, and led execution of the ML product.
Outcome: Reduced identification time by up to 50% and improved targeting effectiveness by 30%.
Problem: Medical Science Liaison activity was qualitative, fragmented, and hard to explain upward.
Role: Led discovery, executive interviews, mockups, PRDs, KPI design, and global rollout.
Outcome: Defined 80+ KPIs and deployed executive dashboards across three pharma clients.
Problem: Regulatory teams were spending expert time on repeatable, rules-driven work.
Role: Managed five analysts, mapped automation opportunities, and designed RPA workflows.
Outcome: Improved efficiency across 40+ workflows and scaled transformation delivery.
Problem: Group dining bookings were offline, unstructured, and difficult for venues to convert.
Role: Built a full-stack MVP through market research, user interviews, and AI-assisted prototyping.
Outcome: Improved operational efficiency by 70% and validated demand with early partner interest.
The throughline is practical strategy: turn ambiguous business questions into analytics products, prototypes, dashboards, and operating change.
Focus: Agentic AI, machine learning, statistics, decision analytics, and business strategy.
Impact: Built a booking MVP that converted offline group dining demand into structured workflows.
Impact: Owned $900K in delivery while leading KOL analytics engagements and analyst teams.
Impact: Led discovery, KPI definition, dashboard design, and global deployment for Medical Affairs analytics.
Impact: Managed automation delivery across regulatory business processes and 40+ workflows.
Impact: Built KOL identification and de-duplication models for profiling 50K+ doctors.
Coursework at Thayer sharpened how I evaluate technology, model decisions, and translate engineering trade-offs into product and business strategy.
How to test hypotheses rigorously, interpret variation, and use regression and experimental design to support decisions — not just report numbers.
How to think in segments, positioning, and customer value — framing products around who they serve, why they win, and how go-to-market choices shape adoption.
How to evaluate emerging technologies against business value, not hype — mapping capability gaps, timing risks, and investment priorities into a roadmap teams can actually follow.
How to move from data to predictive models with intention — feature selection, bias–variance trade-offs, and when ML adds value versus when simpler methods are the better call.
How to formulate business constraints as optimization problems — using linear and integer programming to recommend actions, not just describe what happened.
How pricing connects to value capture, competitive dynamics, and willingness to pay — designing strategies that align revenue goals with customer perception and market structure.
Systems thinking for throughput and bottlenecks — how process design, capacity planning, and operational constraints shape what products and services can scale in practice.
How to structure choices when outcomes are probabilistic — decision trees, expected value, and sensitivity analysis to act with clearer trade-offs instead of waiting for certainty.
End-to-end project delivery: scoping a real analytics problem, building and iterating on models or AI workflows, and presenting results stakeholders can use to make decisions.
How to read financial statements, connect operational choices to P&L impact, and speak the language executives use to evaluate investments, margins, and long-term viability.
How to apply product, analytics, and strategy skills in a live business setting — scoping ambiguous problems, delivering under real constraints, and understanding how organizations actually decide and ship.
How strategy and culture interact — aligning incentives, leading through change, and building organizations where people can actually execute on the plan.
How to move from strategic intent to execution — breaking goals into initiatives, managing trade-offs across teams, and keeping momentum when conditions shift.
Still deciding the third elective for this semester.
These are grouped by how I work: shaping products, modeling decisions, building prototypes, and communicating strategy.