Director, Client Data Science
You will: Own Client Quarterly Business Review (QBR) automation end to end. Build the systems, using AI alongside other automation tooling, that turn QBR prep, including the slides and commentary, from a manual production process into something that runs largely on its own.
Automate Foodsmart’s recurring weekly and monthly client reporting in partnership with Data Engineering, so routine reporting runs on its own instead of eating analyst time every cycle.
Own the RFP data function: providing the analysis, benchmarks, and data cuts that Sales and the proposal team need, without owning the writing or the proposal process itself.
Create and own a standard system of client SLAs: which commitments we can validate and stand behind today, and act as the point person for evaluating whether to adopt new SLA asks from prospects or clients.
Build and maintain the foundational set of metrics Foodsmart uses externally: consistent, defensible numbers that Sales, Customer Success, RFP responses, and QBRs can all draw from rather than each function calculating its own version.
Be conversant in Foodsmart’s clinical ROI story well enough to represent it confidently in QBRs and client conversations, partnering with our Senior Director of Clinical Intelligence on the underlying technical model and any deep-dive analysis.
Act as the data consultant behind Foodsmart’s external narrative: make sure it’s built on accurate data, and help sharpen how it’s told, without owning the story or the external materials yourself.
Pull in expertise from across the analytics team, including member engagement and clinical outcomes, as needed to assemble a complete and accurate external story, rather than trying to own every domain’s data yourself.
Partner directly with Customer Success and Sales leadership as their embedded analytical lead, attending cadences and understanding what clients and prospects are actually asking for.
Represent Foodsmart directly in front of clients and prospects when needed, whether that’s presenting a QBR, supporting a sales call with data, or fielding technical questions during a deal.
You are: An operator who thrives in flat, fast-moving teams, comfortable owning a broad mandate with minimal oversight.
A confident external communicator. You’re comfortable being in the room with clients, prospects, and partners, translating data into a clear, credible story on the spot, not just producing the underlying analysis.
A systems thinker who’d rather build the infrastructure that makes dozens of QBRs easy than manually produce each one well. You see repetition as a signal to automate, not grind through.
A trustworthy data steward. When you say a metric or an SLA is solid enough to put in front of a client or a payer, that means something, and you’re comfortable being the person who has to actually validate that before it goes external.
A full-stack analytics practitioner, strong across analytics engineering (dbt, semantic layer), BI and reporting, and enough data science fluency to know when a number needs real rigor behind it before it goes external.
Deeply fluent with AI-native tooling. You default to using Claude, Claude Code, and in-BI AI agents to build and automate, and you have a point of view on how to use them to replace manual reporting work specifically.
Someone who treats AI as a real lever on your own output. You default to using it to build, automate, and extend your reach, and you’d rather scale this area through leverage than through headcount.
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 BI, with real experience owning client-facing or external-facing reporting. 4+ years leading teams
Experience in an equivalent role at another healthtech company, ideally one with a similar mix of clinical, operational, and commercial reporting demands.
Direct experience supporting RFP processes with data and analysis, ideally in a healthcare, Medicaid, or payer-adjacent context.
Experience building or owning SLA frameworks, or a clear point of view on how to structure one from scratch.
Comfort discussing clinical ROI and outcomes methodology at a business level. You don’t need to own the technical model, that sits with our Senior Director of Clinical Intelligence, but you need to represent it credibly to clients and prospects.
Experience partnering directly with Sales and Customer Success leadership as their analytical lead.
Expert-level proficiency in SQL and working knowledge in Python.
Deep, production-level experience with dbt, including source and metric modeling, testing, documentation, and semantic layer design.
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 automating recurring reporting deliverables (QBRs, board decks, client-facing dashboards) using AI or workflow tooling, not just building the underlying dashboard.
Excellent written and verbal communication skills, with genuine comfort presenting data-driven narratives to external, non-technical audiences.