Lead Data Scientist at Hilberts

HILBERT IS A SCALABLE, DATA SCIENCE-FIRST GROWTH ENGINE THAT GIVES B2C TEAMS PREDICTIVE CLARITY INTO USER BEHAVIOR, REVENUE DRIVERS, AND THE ACTIONS THAT DRIVE SUSTAINABLE GROWTH. FULLY AGENTIC BY DESIGN, HILBERT SHRINKS MONTHS-LONG DECISION CYCLES TO MINUTES. From Fortune 10 enterprises to beloved brands like FreshDirect, Blank Street, and Levain Bakery, operators run their growth on Hilbert. We're also co-building alongside leading AI companies. We're looking for a Lead Data Scientist who thinks in systems, understands B2C business problems deeply, and can build the models and analyses that power real growth outcomes for the world's largest consumer companies — all with the ownership and urgency of a founder. This is not a "build models in isolation and hand off a notebook" role. You'll own the entire data science function — from problem framing through model development through business impact — and you'll do it for enterprise customers where the stakes are real and the feedback loop is tight. If you understand why a recommender system matters to a retailer's P&L, can design a configurable ML system that works across customers, and can explain causal impact to a room of executives with clarity and conviction, we want to meet you. THE ROLE You'll work directly with the founding team and across engineering, product, and GTM to define, build, and scale the data science systems at the heart of Hilbert. You'll be hands-on daily — building models, running analyses, interrogating data — but you'll also set the scientific direction, establish rigor, and grow the team. B2C is our world. The problems we solve — demand prediction, customer lifecycle, personalization, activation — require someone who understands these domains deeply and can translate business context into model design decisions. The environment is high-autonomy and high-ambiguity. Data is often messy, incomplete, or limited. You thrive in exactly those conditions. WHAT YOU'LL DO: Build — hands-on, every day - Design and build ML models that power core product capabilities: recommendation systems, search relevance, customer segmentation, demand forecasting, and activation optimization - Develop configurable, multi-tenant model architectures that adapt to different customer contexts, data availability, and business requirements without being rebuilt from scratch - Create meaningful models with the data that's actually available — not the data you wish you had. You know how to extract signal from limited, noisy, or sparse datasets - Design and run rigorous A/B tests and experimentation frameworks — including understanding when A/B testing is insufficient and causal inference methods are required - Deliver analyses that drive decisions — not dashboards that collect dust. You connect model outputs to business outcomes and communicate them with clarity - Apply causal reasoning rigorously — you know the difference between correlation and causation, you design analyses that surface true drivers, and you flag when others confuse the two Lead — set direction and raise the bar - Define and own the data science roadmap in partnership with the founding team - Think in systems. You don't build isolated models — you design interconnected systems where recommendation, segmentation, scoring, and activation reinforce each other. You see how the pieces fit together and where leverage exists - Frame business problems as data science problems — and know when a simpler analysis beats a complex model - Set scientific standards — validation methodology, experiment design, documentation, reproducibility - Prioritize across competing demands, keeping the team focused on highest-impact work - Communicate results, tradeoffs, and strategic recommendations clearly to founders, customers, and non-technical stakeholders - Be the tiebreaker on methodology — when the team debates approaches, you bring clarity Grow — build the team and the culture - Hire, mentor, and develop data scientists as the team scales - Create an environment of scientific rigor without academic slowness — ship, validate, iterate - Build processes that work at startup speed — reviews and checkpoints that improve quality without killing velocity - Identify capability gaps and build the team to fill them - Lead by example: the team sees you in the data, in the code, in the hard problems — not just in planning docs WHO THRIVES IN THIS ROLE We care about how you think about problems, how you connect models to business impact, and how you make others around you sharper. THE PROFILE: - You're a systems thinker. You don't optimize one metric in a vacuum — you understand how models, data flows, customer behavior, and business outcomes connect. You design for the system, not the silo - You have deep B2C business knowledge. You understand the problems that consumer businesses actually face — customer acquisition vs. retention economics, lifecycle dynamics, basket composition, churn drivers, promotional cannibalization, channel attribution, demand elasticity. You've lived in this world and it informs how you build - You've built recommendation, search, and/or customer-based ML models in production — not just in research. You understand collaborative filtering, content-based methods, ranking systems, segmentation, propensity modeling, and when each applies - You build configurable systems, not one-off models. You've designed model architectures and pipelines that work across multiple customers, segments, or contexts with tunable parameters — not bespoke rebuilds for every use case - You create value from limited data. You know how to make pragmatic modeling choices when data is sparse, noisy, or cold-start. You reach for the right level of complexity — not the most impressive one - You're rigorous about causality. You understand causal inference methods — difference-in-differences, instrumental variables, propensity scoring, synthetic controls — and you apply them when correlation isn't enough. You design A/B tests properly and know their limitations - You communicate with clarity and conviction. You can present a causal analysis to a C-suite audience and make it land. You can write a one-pager that changes a decision. Communication is not a nice-to-have here — it's the job - You take ownership at the team level. You don't just own your own models — you own the team's impact. If an analysis is weak or a model underperforms, you treat it as your problem - You thrive in ambiguity. Problem definitions shift. Data availability surprises you. You bring structure to chaos without killing speed — and you coach the team to operate the same way - You move at startup speed and expect the same from your team. You understand what it means to be available, responsive, and biased toward action in a fast-moving, early-stage environment STRONG PLUSES: - Strong Python proficiency — you write production-quality code, not just notebook prototypes - Experience with experimentation platforms and A/B testing infrastructure at scale - Exposure to retail, e-commerce, CPG, or marketplace data environments - Prior experience as a data science lead, principal data scientist, or founding data scientist at an early-stage or high-growth company - Track record of hiring and developing data scientists — not just managing them - Familiarity with modern data and ML infrastructure — feature stores, orchestration, model serving, monitoring YOU MIGHT BE: A senior data scientist at a B2C company who's tired of optimizing one metric and wants to build the whole system. A data science lead at a startup who's ready for higher stakes and more ownership. Someone who came up through quantitative consulting or economics and moved into applied ML because they wanted to see models drive real decisions. A founding data scientist who built the function at their last company and wants to do it again where data science is the product. What matters: you think in systems, you understand the business, you ship models that work with real data, and you communicate impact — not just methodology. Location SAN FRANCISCO, WITH OCCASIONAL TRAVEL FOR TEAM MEETS, OFFSITES OR CUSTOMER ENGAGEMENTS. COMPENSATION COMPETITIVE SALARY + EQUITY PACKAGE, COMMENSURATE WITH EXPERIENCE. PERFORMANCE-BASED BONUSES TIED TO PROJECT MILESTONES AND CUSTOMER IMPACT. THE HIRING JOURNEY SHORT FORM → INTRO CALL → TECHNICAL WORKING SESSION → TEAM CONVERSATIONS → OFFER FAST, HUMAN, NO BUREAUCRACY.