Director, Member Data Science

You will: Own the analytical strategy for the end-to-end marketing funnel across both activation and retention: from marketable lives to lead generation to omni-channel engagement strategy, to visit completion, re-engagement, and the reactivation campaigns that bring lapsed members back into their care journey. This includes our call center function (outbound rep allocation, inbound referral scheduling, ZCC data) and member lifecycle (Customer.io journey performance).

Own the product analytics domain across both activation and retention: onboarding funnel, sign-up conversion, in-app engagement through a member’s completed first appointment, and the ongoing booking and in-app experience that shapes whether members keep coming back. Partner with the product team as their embedded analytical lead, attending product cadences and co-owning the product analytics roadmap.

Serve as the executive-facing owner of the member funnel and retention performance narrative: explaining why marketable lives, funnel conversion, initial visit completion, and retention moved, what levers drove the result, and what to double down on. Partner directly with senior leaders as the analytical voice for the member journey.

Design and lead Foodsmart’s experimentation program across marketing, product, and retention, including test design, causal inference methods, readout discipline, and the intake process for stakeholder-driven test ideas. Own the StatSig implementation and serve as the internal expert on experiment instrumentation, StatSig configuration, and results interpretation.

Own and evolve our attribution framework, including scheduling episode attribution, multi-touch attribution, and media mix modeling as Foodsmart’s channel portfolio grows.

Partner with Clinical Operations leadership, whose Registered Dietitian network is a key driver of member retention.

Own and evolve the dbt data models across marketing, product, and retention, from raw source modeling through metrics, ensuring data quality, test coverage, documentation, and a semantic layer that makes self-service trustworthy. This is a core craft expectation of this role, not a secondary responsibility.

Engineer context into our semantic layer and BI environment (Omni) so that stakeholders and AI agents can reliably self-serve answers across the member journey. You treat context engineering (writing descriptions, defining metrics, curating what’s exposed) as a first-class part of your job.

Lead and develop a small team: setting technical direction, reviewing work, coaching toward increasingly independent ownership of their part of the member journey, and helping the team find real leverage through AI tooling rather than doing more manual work.

You are: An operator who thrives in flat, fast-moving teams. You need minimal guidance to drive outcomes and default to taking ownership rather than waiting for direction.

A domain-owning leader who is comfortable being the single point of accountability for a critical, company-level outcome and the executive-facing voice on its performance.

A rigorous experimentalist who treats causal inference as a core craft, not a buzzword. You have a point of view on what makes a test trustworthy and how to teach causal thinking to business partners.

A strategic partner who can translate a high-level business problem into a concrete analytical roadmap and influence senior leaders, including C-level executives, across product, marketing, clinical, and finance.

A full-stack analytics practitioner, strong across analytics engineering (dbt, semantic layer), business intelligence and dashboarding, and data science (predictive modeling, causal inference, optimization). You don’t silo into pure stats/Python work, and you understand that durable insight requires owning the data foundation, not just the models on top of it.

Deeply fluent with AI-native tooling. You see tools like Claude, Claude Code, and in-BI AI agents as a core part of how you and your team get leverage, and you have a point of view on how to engineer the context and semantic layer that makes AI-driven self-service trustworthy.

Genuinely energized by scaling a small team’s output through AI rather than through more headcount. You’d rather solve “how do we get 3x the leverage out of this team” than “how do I get budget for 2 more hires,” and you want the person you manage to feel that same energy.

A capable, motivated people leader who wants to stay predominantly hands-on. This is not a pure management-track role. We’re looking for someone who leads by building and reviewing alongside their team, not by stepping back from the work.

You have: Bachelor’s degree, ideally in a quantitative or technical field (e.g., Economics, Statistics, Computer Science, Operations Research, Applied Mathematics); Master’s degree is a plus.

12+ years of experience in data science, analytics, or experimentation, with a proven track record of driving measurable impact on growth, acquisition, or lifecycle outcomes. 4+ years leading teams.

Experience partnering directly with senior and executive stakeholders (VP-level and above) as the analytical voice for a business area, ideally including some experience leading or mentoring other data scientists or analysts.

Deep, hands-on expertise in experimentation and causal inference. You have designed and interpreted rigorous tests (A/B, quasi-experimental, geo-lift) and can defend methodology choices under scrutiny.

Strong background in attribution modeling (scheduling episode, multi-touch attribution, media mix modeling) and a clear point of view on the tradeoffs between approaches.

Experience owning lifecycle analytics, ideally including hands-on work with Customer.io, Braze, Iterable, or a similar platform.

Hands-on experience with product analytics instrumentation: event tracking, funnel analysis, and experimentation platforms (Statsig, Amplitude, Mixpanel, or equivalent). You have a point of view on what good product measurement infrastructure looks like.

Experience with call center or contact center analytics is a plus. We leverage Zoom Contact Center (ZCC) for our outbound and inbound scheduling teams.

Expert-level proficiency in SQL and strong proficiency in Python (pandas, scikit-learn, statsmodels, etc.).

Deep, production-level experience with dbt, including source and mart-layer modeling, testing, documentation, and semantic layer design. You have owned a dbt project end-to-end, not just contributed to one.

Experience with context engineering for BI and AI self-service: writing semantic layer definitions, metric descriptions, and data model documentation that enables reliable AI-assisted querying (Omni, Looker, or equivalent).

Proven fluency with AI-native developer and analyst tooling (Claude, Claude Code, Cursor, Hex AI Agent, Omni AI, or equivalent) used in production analytical workflows.

Experience working in marketplace business models and/or adjacent to healthcare, Medicaid, or a similarly regulated domain is a plus but not required.

Excellent communication skills. You can distill complex models, test results, and funnel diagnostics into clear, actionable recommendations for executive, product, and marketing-leadership audiences.