What I did

• Collaborated with external research partners (incl. Imperial College London) to define data requirements and quality criteria for multi-institutional work.
• Translated ambiguous research objectives into structured analytical approaches, aligning on success metrics and validation criteria.
• Documented analytical methodologies and communicated findings to clinical researchers, data scientists, and lab staff.
• Reviewed end-to-end data workflows and supported process improvements using Python and SQL.

Tools

Objective

Build a clear BA-to-build blueprint for budgeting: scenario planning, variance tracking, and structured expense categorization.

Key contributions

• Elicited needs through structured interviews and problem framing; translated them into 25+ functional requirements.
• Designed logical data models and defined business rules for variance tracking, expense categorization, and multi-scenario comparison.
• Produced functional specifications and process flows to bridge business expectations with technical implementation.

Artifacts

Problem

Users want simple, actionable wellness guidance—not fragmented insights across multiple apps and metrics.

What I did

• Ran user research with 15+ participants and competitive analysis of 5 wellness platforms to identify gaps and differentiate the product.
• Designed user journeys, wireframes, and functional specifications for a personalized recommendation engine (“Today’s Focus”).
• Facilitated iterative feedback sessions with a 20-user beta, refining usability and increasing daily habit completion by 30%.

Outputs

Research and discovery Full plan

Problem

Career changers struggle to identify the highest-impact skills from noisy job postings and generic course catalogs.

Solution

• Scraped and structured job-market requirements to surface high-demand skills.
• Generated a skill-gap view and learning roadmap tied directly to target roles.
• Prototype dashboard: upload CV → receive recommendations + action plan.

Full Plan