Resume

AI & Data Science Intern

• —Defined and scoped the AI Data Readiness Checker, translating 14 data quality dimensions (completeness, bias, consistency, and more) into measurable product features for enterprise datasets.
• —Partnered with stakeholders to prioritize which dimensions to surface first, balancing enterprise usability, technical feasibility, and time-to-value.
• —Engineered Snowflake-integrated Python pipelines and a supporting dashboard to profile 100+ large-scale tables, reducing manual QA effort by ~30%.
• —Presented the prototype to senior leadership, securing support to develop the tool as a client-facing product to accelerate ML project launches.

Product Analyst Intern

• —Built and launched a Tableau Health Dashboard consolidating 10+ marketplace KPIs (CTR, eCPM, win rate, margin), giving leadership daily visibility into supply–demand health across millions of ad auctions.
• —Developed automated SQL + Python pipelines in BigQuery to monitor revenue trends and auction outcomes, streamlining reporting and enabling faster product and marketplace decisions.
• —Analyzed auction dynamics across thousands of daily transactions to surface demand and supply-side patterns, collaborating with PMs and engineers to translate insights into product requirements and roadmap prioritization.

Product Manager — TenantBridge

• —Led a 3-person team through the full product lifecycle for TenantBridge, a housing discovery app connecting prospective renters with current tenants — owning problem definition, user research, and MVP scoping.
• —Conducted 20+ user interviews to identify unmet needs, translating findings into product requirements, MVP scope, and prioritized tradeoffs under ambiguity.
• —Validated problem hypotheses through follow-up user feedback and market research, iterating on feature scope and positioning based on learnings.
• —Created high-fidelity Figma mockups and a pitch deck to communicate the MVP roadmap and guide tradeoffs across usability, design, and technical feasibility.

Professor Assistant — Data Scientist

• —Collaborated with clinicians and researchers to analyze 450+ patient records, framing technical work around improving diagnosis and treatment planning for catatonia.
• —Engineered an end-to-end ML pipeline in Python (Pandas, Scikit-learn), incorporating validation and imbalance correction to ensure model reliability at clinical scale.
• —Benchmarked predictive models (Random Forest, Logistic Regression, SVC), achieving 75% accuracy and surfacing the strengths and limitations of each approach for real-world use.
• —Identified 15 key clinical features influencing outcomes and presented insights via visual reports (ROC curves, confusion matrices), enabling non-technical stakeholders to make informed treatment decisions.

Engineering Shadow

• —Worked alongside a senior engineer through weekly sessions focused on data systems, data streams, and pipeline architecture — analyzing how infrastructure choices impact product performance at scale.
• —Completed explorations on data pipelines and system design, bringing analyses to discussions around tradeoffs in latency, scalability, and cost.
• —Documented data architecture tradeoffs and decision rationale, linking product priorities to system design.