Staff Data Scientist, Growth

📋 Description

• • Serve as the human explicability layer for Pearl Health’s underwriting models, translating complex ML outputs into clear, persuasive narratives that win provider trust and accelerate sales cycles. You will sit shoulder-to-shoulder with Growth reps on calls, demos, and RFP responses, distilling SHAP values, feature importances, and risk corridors into stories that resonate with physician groups and health-system CFOs.
• • Own the end-to-end design of an interpretability ecosystem that turns model artifacts into reusable, self-service tooling for the Growth team. During peak sales season (Aug–Dec), you will ship weekly iterations of dashboards, notebooks, and GenAI-powered “explainers” that surface why Pearl’s contracts are priced the way they are, while off-season you will harden these prototypes into scalable, versioned libraries.
• • Proactively identify and neutralize risks that threaten deal velocity or model credibility. You will run Monte Carlo stress tests on underwriting outputs, catalog edge-case scenarios, and present mitigation strategies to Sales, Actuarial, and Product leadership two sprints before they become blockers.
• • Translate field feedback into actionable ML roadmap items. Every objection, clarifying question, or “aha” moment from prospects becomes a ticket: new features, alternative risk corridors, or novel data enrichments. You will prioritize these requests using expected revenue impact and engineering cost, then shepherd them through our core ML team’s quarterly planning.
• • Architect data pipelines that ensure Growth stakeholders have clean, timely, and compliant access to lead, touchpoint, and financial datasets. You will set SLAs for freshness (≤6 hrs), define reproducible ETL standards in dbt, and enforce unit-test coverage ≥90% for any code that touches provider-level PII.
• • Deepen Pearl’s competitive intelligence by continuously benchmarking our value-based care contracts against LEAD, REACH, MSSP, and Medicare Advantage benchmarks. You will maintain a living compendium of market pricing, network adequacy rules, and quality benchmarks that Sales can weaponize in real time.
• • Mentor junior data scientists and analysts across Growth and Data Science, instituting code-review rituals, brown-bag sessions, and pair-programming rotations that elevate technical rigor and knowledge sharing.
• • Evaluate third-party vendors and data assets (claims feeds, SDOH enrichments, GenAI copilots) for ROI, privacy posture, and integration lift. You will produce concise vendor scorecards that allow leadership to make go/no-go decisions within one week.
• • Champion Pearl’s values—collaborate to innovate, trust through transparency, serious impact with a big heart—by openly sharing model limitations, celebrating small wins with GIFs in Slack, and volunteering to onboard new teammates even when bandwidth is tight.