Sr. Data Scientist II

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

• Strong foundation in Bayesian inference and probabilistic modeling — e.g. hierarchical / multilevel models, state-space and time-series models, graphical models, MCMC/HMC, variational and other approximate inference

• Experience applying these methods to real, messy, production data — not only research or coursework

• Comfort reasoning about uncertainty, calibration, and model validation

• Facility with large or structured datasets and the computational side of inference at scale

• Strong Python, and fluency in a modern probabilistic-programming and numerical-computing stack — NumPyro, PyMC, Stan, JAX, dynamax, or similar. We hire on the ideas, not on exact tooling

• Track record of shipping statistical models into production

• BS or PhD in Statistics, Math, Economics, Physics, CS, or a related quantitative field

• 8+ years of industry experience as a data scientist (or equivalent role/work) with a BS in the above-mentioned areas, or 5+ years of industry experience with a PhD in a quantitative field

• Clear communication with both technical and non-technical audiences

Nice to Haves:

• Diagnosing and debugging large Bayesian models — convergence and divergence issues, pinning down which part of a big model is misbehaving, and knowing which inference method to reach for

• Weighting a non-representative survey or panel sample up to a known population, and a feel for where those adjustments break down

• Hierarchical models spanning multiple crossed or overlapping groupings — relationships that bridge hierarchies, not just a single nested tree

• Experience with graph or network models, or modeling relational / graph-structured data

• Measurement-error modeling, or reconciling multiple imperfect data sources

• CPG / FMCG / retail experience, or work with user-level purchase or panel data

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