About the job
Join Hilbert, a pioneering data science-driven growth engine that empowers B2C teams with predictive insights into user behaviors, revenue drivers, and sustainable growth strategies. Our innovative approach compresses lengthy decision-making processes into mere minutes.
Trusted by Fortune 10 enterprises and beloved brands like FreshDirect, Blank Street, and Levain Bakery, Hilbert is the backbone of their growth strategies. We are also collaborating with leading AI companies to push the boundaries of what’s possible.
We are seeking a talented Data Scientist who possesses a deep understanding of B2C business challenges, develops actionable models using real-world data, and delivers impactful analyses that facilitate significant growth outcomes — all with the initiative and urgency typical of a founder.
This is not a role where you simply receive tasks; you will take ownership of problems from start to finish — from problem framing and modeling to measuring impact — for enterprise clients where the stakes are high and feedback is rapid. If you understand the nuances of churn analysis for different sectors, can create effective recommendation systems from sparse data, and can clearly communicate your causal analysis to clients, we want to meet you.
ROLE OVERVIEW
You will closely collaborate with the founding team, engineering, product, and go-to-market teams to enhance the data science systems integral to Hilbert. Daily responsibilities include building models, conducting experiments, analyzing data, and producing analyses that influence key decisions. Our focus is B2C, and the challenges we tackle — such as demand forecasting, customer lifecycle management, personalization, and activation — require an individual who can translate business contexts into effective modeling choices. You will thrive in a high-autonomy, high-ambiguity environment where data is often messy, incomplete, or scarce.
Key Responsibilities:
Develop ML models that enhance core product features: recommendation systems, search relevance, customer segmentation, demand forecasting, and activation optimization.
Contribute to configurable, multi-tenant model architectures that adapt to various customer contexts and business needs, avoiding the need for custom solutions for each case.
Build effective models using available data — leveraging limited, noisy, or sparse datasets while determining the appropriate level of complexity.
Design and implement rigorous A/B tests and recognize when causal inference methods are necessary.
