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
Skills & Requirements
- 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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