Senior Data Scientist with 5+ years at the intersection of people, behavior, and statistics. I've built attrition models, designed workforce experiments, and shipped ML systems used by executive teams. Whether the domain is People Analytics or Product Data Science, I bring the same thing: rigorous methods, clean code, and a clear story.
I specialize in causal inference, predictive modeling, and experimentation, applied to domains where the stakes are high and the data is messy. My background spans People Analytics at Publicis Sapient and Korn Ferry, workforce intelligence at Infosys, and I'm completing an MS in Business Analytics at the University of Cincinnati (2026).
I can run a PSM analysis and explain it to leadership the same afternoon. I build things that actually get used: Tableau dashboards for executive reviews, flight-risk scores operationalized into retention playbooks, A/B tested sourcing strategies with documented causal impact.
Outside of work, I box, and I mean that seriously. The discipline, the structure under pressure, the need to stay technical when you're tired. It maps onto data science in ways that still surprise me.
I hike whenever the Bay Area weather cooperates (which is often enough). I read broadly, organizational behavior, behavioral economics, the occasional novel. I've been a teaching assistant and mentor at UC, and genuinely enjoy helping people build the instinct for good analytical judgment, not just the mechanics.
Led end-to-end People Analytics across the AI & Cloud hiring portfolio, building attrition forecasting models (85% accuracy, enabling 30% reduction in emergency hiring costs) to designing causal inference studies on sourcing strategies that cut time-to-fill by 25%. Operationalized a 15+ metric measurement framework into Tableau dashboards reviewed by executives weekly. Also led responsible AI work: algorithmic fairness audits, bias testing protocols, and human-oversight guidelines reviewed by Legal and HR leadership.
Built predictive models for executive hiring success and retention risk on 3,000+ candidate profiles; outputs directly shaped C-suite slate recommendations for clients in tech, finance, and healthcare. Ran SQL funnel diagnostics that identified bottlenecks reducing executive search cycle time by 30%. Delivered benchmarking under 24 to 48 hour windows for time-sensitive engagements.
Built and operationalized flight-risk scoring on 20,000 employee records. Scores fed directly into HR retention playbooks, reducing attrition by 12%. Built compensation benchmarking models across 15+ BUs. Salary elasticity analysis improved offer conversion by 11%. Ran recruiter workload analysis across 200+ recruiters, surfacing reallocation strategies that improved productivity by 18%.
Analyzed 12,000 employee records across 4 delivery centers to model onsite/offshore cost structures. Recommendations reduced staffing costs by 10 to 12%. Built SQL reporting infrastructure tracking 5,000+ monthly hiring activities with automated Tableau dashboards for requisition aging and offer acceptance tracking.
I'm actively exploring Senior Data Scientist and People Analytics roles where I can pair statistical depth with real business impact. If you're building something where the human data is as important as the product data, I'd love to talk.