Lead Data Scientist

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 Fresh Direct, 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.

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

• Ide…