30+
Products owned
Product Management for Data & AI Products
09/2021 – Present Deloitte Consulting
Owned the roadmap for a portfolio of 30+ data analytics products, including AI-powered products with LLM integration and semantic search. Managed product owners and project managers across concurrent initiatives. Certified Project Management Professional (PMP).
AI
Products delivered
PMP
Certified
Zero-to-Launch Product Management
Requests often arrived already framed as solutions. I took a step back to understand and validate the problems behind them, then tested potential solutions with customers using prototypes, scoped first releases that delivered value quickly, and launched in stages against metrics established in advance where possible.
Problem
Solution
Scope
Launch
Framing the Problem
Met with customers to understand their day-to-day work, especially what was time-consuming or repetitive, and to learn about recurring problems they found intractable. These discussions established whether a problem was costing meaningful time and money or was simply an annoyance already worked around at little cost.Met with customers to understand their day-to-day work, especially what was time-consuming or repetitive, and to learn about recurring problems they found intractable. Discussion established whether a problem was costing meaningful time and money or was simply an annoyance already worked around at little cost.
Testing the Solution
Once my team and I had a candidate solution, often after an analysis of alternatives, wireframes and prototypes went in front of customers before engineering committed. Sessions focused on whether customers could complete the task without being told how, where their instincts diverged from the design, and requirements still undefined.
Scoping the Build
Scoped first releases to deliver value quickly, then added value incrementally through later releases. Scope was set by risk, testing the assumption most likely to undermine the product. Success metrics were defined before building.
Launching and Measuring
Releases were staged: internal, then a pilot group, then broader rollout. Each stage was measured against the metric set before launch, and the result decided whether to iterate or expand to more users.
Leadership and Delivery
Owning the Vision and Roadmap
Owned the product vision and roadmap for a portfolio of analytics and AI products. Prioritized features by expected value and effort to build, and sequenced releases against dependencies and team capacity. Every roadmap item traced back to a validated problem. Communicated the vision so customers and the delivery team shared the same understanding of what a product was for. Worked alongside the product owner to keep the customer's voice in front of the team.
Working Through Product Owners and Project Managers
Managed product owners and project managers across concurrent initiatives, setting direction and priorities while they owned backlog detail, schedule, and dependencies. On key initiatives, served as product manager, product owner, and project manager, defining scope and requirements and running delivery with engineering, data science, and design.
Risk and Transparency
Fostered an environment where team members raised risks early and throughout projects. Ensured each risk had an impact, owner, mitigation and trigger. Kept clients informed of timelines, assumptions, and risks to align expectations.
Selected Work
Supervised Machine Learning for Scientific Coding Assistance
Delivered an ML-based classification tool that recommends scientific codes for grant applications, achieving over 85% accuracy, recall, and precision on infectious disease codes and reducing manual curation effort for staff.
Client work is described at a high level, and specific engagement details are intentionally omitted.
