Lead Data Scientist

Hilbert's AI•San Francisco, CA

22h•Onsite

About The Position

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. 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. We care about how you think about problems, how you connect models to business impact, and how you make others around you sharper.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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