[8BE] Senior Data Scientist, Probabilistic Modeling (Architecture, Adjacent)

Role overview

A senior data science position centered on probabilistic modeling and its application to architecture-adjacent problems. The role sits within a data science and engineering organization and supports decision-making where uncertainty quantification and statistical reasoning matter. It is suited to an experienced practitioner who can translate modeling work into concrete guidance for architectural choices.

Responsibilities

- Design, build, and validate probabilistic models that inform architectural decisions and adjacent engineering questions - Apply Bayesian, statistical, or other uncertainty-aware methods to real datasets, product contexts, or system behaviors - Partner with architecture and engineering teams to scope modeling problems and integrate outputs into their workflows - Communicate results, assumptions, and confidence levels clearly to both technical and non-technical stakeholders - Mentor and review work from other data scientists in line with a senior individual-contributor grade

Requirements

- Senior-level experience as a data scientist with a track record of shipping modeling work that influences real decisions - Strong grounding in probabilistic modeling, Bayesian inference, or related statistical and machine learning techniques - Ability to collaborate closely with architecture or systems teams and translate between modeling concerns and engineering concerns - Solid programming and data-handling skills needed to build and maintain reliable pipelines - Clear written and verbal communication, including the ability to document assumptions and limitations

Nice to have

- Background working directly with software, data, or solution architects on modeling questions - Familiarity with applying probabilistic methods to system reliability, performance, capacity, or design trade-offs