ML Engineer / Data Scientist - Core

Hilbert is building the demand intelligence platform used by world-class B2C companies — including the world's largest retailer — to unlock compounding growth outcomes. The product sits at the intersection of AI, data, and commercial activation for retail and e-commerce: recommendation engines, demand forecasting, customer lifecycle models, and activation systems that must work across wildly different retailers, data environments, and business contexts.

This isn't single-tenant model building. Hilbert's ML systems are configurable and production-grade, designed to generalize across Fortune 500 enterprises and consumer brands alike. The team is small, talent-dense, and low-ego. Every model built and every pipeline shipped has direct, measurable impact on enterprise revenue.

The Opportunity

Hilbert is looking for an ML Engineer who understands B2C business problems deeply, builds models and pipelines that work with real-world data, and delivers systems that drive real growth outcomes. This is not a "receive a ticket, train a model, hand off a notebook" role. You will own problems end-to-end — from framing through modeling through production deployment through impact — for enterprise customers where the stakes are real and the feedback loop is tight.

The data is often messy, incomplete, or limited. The problem definitions shift. The customers are large and demanding. 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 runs reliably in production, and can walk a customer through a causal analysis with clarity and conviction, this is built for you.

What You'll Do

• •      Build and deploy ML models and pipelines powering 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 account
• •      Own models through to production, working with engineering on serving, monitoring, and reliability rather than handing off prototypes
• •      Extract signal from limited, noisy, and sparse datasets; make pragmatic modeling choices and reach for the right level of complexity given the data that actually exists
• •      Design and run rigorous A/B tests; know when A/B testing is insufficient and when causal inference methods are required
• •      Apply causal reasoning rigorously — surface true drivers, understand the difference between correlation and causation, and flag when others confuse the two
• •      Connect model outputs to business outcomes and communicate findings with clarity to founders, teammates, and customers
• •      Think in systems — understand how recommendation, segmentation, scoring, and activation interact and design work to fit the broader picture

You Should Have

• •      Production ML engineering experience — you write clean, testable Python, and your work ends when the system is running and delivering value, not when the notebook is done
• •      Strong B2C business knowledge across customer acquisition and retention economics, lifecycle dynamics, churn drivers, demand elasticity, and promotional dynamics
• •      Experience building recommendation, search, or customer-based ML systems including collaborative filtering, content-based methods, ranking, segmentation, and propensity modeling
• •      Experience building for configurability across multiple customers, segments, or contexts rather than rigid single-purpose implementations
• •      Rigorous understanding of causal inference methods including difference-in-differences, propensity scoring, instrumental variables, and synthetic controls
• •      Strong communication skills — you can present an analysis to a non-technical audience, write a one-pager that changes a decision, and explain your reasoning not just your results
• •      Comfort operating in high-autonomy, high-ambiguity environments at startup speed

Nice to Have

• •      Experience with ML infrastructure including feature stores, model serving, orchestration, monitoring, or retraining pipelines
• •      Experience with experimentation platforms and A/B testing infrastructure
• •      Exposure to retail, e-commerce, CPG, or marketplace data environments
• •      Background in economics, econometrics, or quantitative social science
• •      Experience at early-stage startups or high-growth companies where you wore multiple hats

Compensation

Competitive salary and equity. Details shared during the hiring process.

The hiring process is short form, intro call, practical working session, team conversations, and offer. Fast, human, no bureaucracy.