Data Scientist​/ML Engineer

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 Data Scientist who understands B2C business problems deeply, builds models that work with real-world data, and delivers analyses that drive real growth outcomes — all with the ownership and urgency of a founder.

This is not a "receive a ticket, train a model, hand off a notebook" role. You'll own problems end-to-end — from framing through modeling through impact — for enterprise customers where the stakes are real and the feedback loop is tight. If you understand why churn analysis matters differently for a grocery retailer versus a fashion marketplace, can build a recommendation system that works with sparse data, and can walk a customer through your causal analysis with clarity and conviction, we want to meet you.

You’ll work directly with the founding team and alongside engineering, product, and GTM to build and improve the data science systems at the heart of Hilbert. You’ll be hands‑on every day — building models, running experiments, interrogating data, and delivering analyses that change decisions. B2C is our world. The problems we solve — demand prediction, customer lifecycle, personalization, activation — require someone who understands these domains and can translate business context into modeling choices.

The environment is high‑autonomy and high‑ambiguity. Data is often messy, incomplete, or limited. You thrive in exactly those conditions.

• Build ML models that power core product capabilities:
  recommendation systems, search relevance, customer segmentation, demand forecasting, and activation optimization

• Contribute to configurable, multi‑tenant model architectures that adapt across different customer contexts, data availability, and business requirements — not bespoke rebuilds for every use case

• Create meaningful models with the data that’s actually available — not the data you wish you had. You extract signal from limited, noisy, or sparse datasets and reach for the right level of complexity

• Design and run rigorous A/B tests — including understanding when A/B testing is insufficient and causal inference methods are required

• Apply causal reasoning rigorously — you know the difference between correlation and causation, you surface true drivers, and you flag when others confuse the two

• Deliver analyses that drive decisions — you connect model outputs to business outcomes and communicate them with clarity to founders, teammates, and customers

• Think in systems. You don’t build isolated models — you understand how recommendation, segmentation, scoring, and activation interact with each other and design your work to fit within the broader system

• Collaborate closely with engineering to take models from prototype to production

• Move fast — prototype, validate, ship, iterate. You’re comfortable with imperfect information and evolving requirements.

We care about how you think about problems, how you connect models to business impact, and how you operate when things are ambiguous.

• You have strong B2C business knowledge. You understand the problems consumer businesses actually face — customer acquisition vs. retention economics, lifecycle dynamics, basket composition, churn drivers, promotional cannibalization, channel attribution, demand elasticity. This knowledge informs how you frame problems and design models

• You’re a systems thinker. You understand how models, data flows, customer behavior, and business outcomes connect. You don’t optimize one metric in a vacuum — you consider second‑order effects and how your work fits the bigger picture

• You’ve built recommendation, search, and/or customer‑based ML models — collaborative filtering, content‑based methods, ranking systems,…