Yidan Hu
Product Data Scientist | Product Analytics & Product Growth | B2B & AI Agent & LLM Specialist | Experiment Design for AI | Causal Inference & Incrementality Measurement
- Role
- Data Scientist - Data & Ai at UMB Bank
- Location
- Austin, TX, US
- LinkedIn followers
- 500 followers
About Yidan Hu
I leverage data science and statistical inference to shape product direction and business decisions, with a deep focus on B2B Enterprise/SaaS environments.My expertise lies at the intersection of product strategy and advanced analytics. I specialize in turning complex data into actionable insights that drive product roadmaps, resource prioritization, and measurable outcomes.With experience across FinTech and Technology, I’ve partnered closely with product and engineering teams on both 0→1 and scaling initiatives. I focus on experimentation (A/B testing), defining North Star metrics, and leveraging customer signals to drive proactive decisions rather than simply reporting results.I excel at bridging the gap between technical complexity and business strategy: translating high-level business questions into rigorous data frameworks and making technical findings accessible to non-technical stakeholders to build alignment and momentum.If you are also interested in Product Data Science, Experimentation, or B2B Product Strategy, let\'s connect!
Experience
Data Scientist - Data & Ai
Sep 2023 — Present
Led deep-dive analyses on banker usage patterns and feature adoption for an internal GenAI recommendation platform, identifying key drivers of engagement and stickiness across customer segmentsDefined and operationalized core product metrics (activation, adoption, response quality, latency), enabling PMs to prioritize feature improvements and deprecate low-impact capabilitiesDesigned and analyzed controlled experiments to evaluate recommendation logic and response strategies, directly informing roadmap trade-offs and resulting in a 20% increase in user acquisitionPartnered closely with PMs and engineers to translate ambiguous business questions into testable hypotheses and data-backed decisions, contributing to an 18% reduction in CACBuilt and validated scalable SQL-based data models and analytics pipelines to support ongoing product performance monitoring and experimentation
Education
University of Connecticut School of Business
Master's Degree, Business Analytics and Project Management
2012 — 2014
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