Staff Data Scientist, Growth Analytics

You will:

• Own the analytical strategy for the end-to-end marketing funnel, from marketable lives to lead generation to omni-channel engagement strategy to visit completion and re-engagement. This includes our call center function (outbound rep allocation, inbound referral scheduling, ZCC data) and member lifecycle (Customer.io journey performance, reactivation, and initial visit completion optimization).

• Own the product analytics domain: onboarding funnel, sign-up conversion, in-app engagement, and the member experience through completed first appointment. Partner with the top-of-funnel 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 marketing and product performance narrative: explaining why marketable lives, funnel conversion, and initial visit completion moved, what levers drove the result, and what to double down on.

• Design and lead Foodsmart's experimentation program across both marketing and product, 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 Growth Marketing leadership as the embedded analytical lead: attending marketing cadences, co-owning the analytics roadmap, and driving insight into action.

• Own and evolve the dbt data models for the marketing and product domains — from raw source modeling through mart-layer 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 marketing and product funnel. You treat context engineering — writing descriptions, defining metrics, curating what's exposed — as a first-class part of your job.

• Drive the narrative, not just the numbers — translate findings into clear, actionable recommendations for executive and marketing leadership audiences.

• Raise the bar for the analytics team on the craft areas central to this role — experimentation design, dbt modeling patterns, and context engineering. You don't manage anyone, but you operate as a technical leader: establishing best practices, reviewing work, and making the team around you better.

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 IC who is comfortable being the single point of accountability for a critical business area and the executive-facing voice on its performance.

• A rigorous experimentalist who treats causal inference as a core craft, not a buzzword — with 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 across marketing, product, 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) — who doesn't silo into pure stats/Python work and understands 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 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.

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.

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

• 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) — and 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 and marketing-leadership audiences.