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Numerator

Data Scientist (Bayesian Inference)

Numerator Chicago, Illinois, United States

Technology, Information and Internet · 5,001-10,000 employees

Sep 25
data-scientist Mid (2-5 yrs) Full-time United States
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About the role

The Data Scientist will contribute to the development and implementation of Bayesian and probabilistic modeling pipelines from research to production. They will collaborate with cross-functional teams to transform proprietary datasets into reliable, production-grade business solutions.

What they look for

Bayesian Inference Probabilistic Modeling Data Science Data Analysis Statistical Modeling Uncertainty Quantification Production-grade Solutions Data Pipelines Analytics Methodologies Cross-functional Collaboration

Requirements

The role requires experience in Bayesian methods and the ability to reason about uncertainty in data models. Candidates should be capable of executing individual tasks and small projects while communicating complex technical results to diverse audiences.

Full description

Numerator is seeking a Data Scientist (Bayesian Modeling) to help build, enhance, and scale data science services across our rapidly evolving data platform. You’ll work on initiatives that turn massive proprietary datasets into impactful, production-grade solutions..

This is a growth-track, product-focused role. You’ll collaborate with Product, Data, and Engineering teams to learn how customer needs translate into data-driven products, analytics methodologies, and new offerings that drive measurable business impact.

How You'll Spend Your Time:

Contribute to the implementation and delivery of Bayesian and probabilistic modeling pipelines, from methodology research through production, with guidance from senior team members

Execute on individual tickets independently and take on small epics with mentorship and guidance

Work closely with Product, GTM, Data, and Engineering to turn models into reliable, production-grade solutions the business can depend on

Actively participate in the team's learning culture (journal club, analysis reviews, standups) and seek feedback to continually level up your craft in Bayesian methods and reasoning about uncertainty

Communicate methods, results, and tradeoffs clearly to both technical and non-technical audiences

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