Numerator

Sr. Data Scientist II

Numerator United States

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

Jul 01
Remote data-scientist Senior (5-10 yrs) Full-time United States
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About the role

Lead the design and delivery of complex Bayesian and probabilistic modeling pipelines from methodology through production. Collaborate with product, data, and engineering teams to translate customer needs into reliable, data-driven solutions.

What they look for

Bayesian Modeling Probabilistic Modeling Data Science Data Analysis Mentoring Technical Leadership Production-grade Solutions Statistical Modeling Data Platform Development Cross-functional Collaboration

Requirements

Requires expertise in Bayesian and probabilistic modeling with the ability to set technical direction for complex problems. Candidates must be capable of mentoring team members and communicating technical tradeoffs to diverse audiences.

Full description

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

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

How You'll Spend Your Time:

Lead the design and delivery of complex Bayesian and probabilistic modeling pipelines, from methodology through production

Set technical direction on hard modeling problems and make the key methodological calls, with a high degree of autonomy

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

Help the whole team get better — mentor other data scientists, share your approach openly, and raise the bar for how the group reasons about uncertainty and Bayesian methods

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

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