Member of Technical Staff, Bayesian Statistics

About the role

Clinical prediction uncertainty is quantified using Bayesian methods embedded in tools that support precision medicine. The role advances multi-modal AI research and deployment to make disease predictable.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Novel Bayesian statistics methods are designed and implemented to quantify uncertainty in clinical predictions for patient outcomes and treatment response.

Production-ready code is translated from machine learning papers to power clinical decision tools such as Ataraxis™ Breast for breast cancer.

Model evaluation frameworks are built to validate performance across complex, multi-modal clinical data, including digital pathology.

Research results are disseminated through co-authoring papers and abstracts for academic and clinical audiences, including conferences and journals.

Multidisciplinary collaboration with engineers and scientists shapes model development and clinical integration for causality, foundation models, and survival analysis.

Junior team members are co-mentored to strengthen research execution and methodological rigor within a flat organizational structure.

Requirements

A PhD degree in statistics or machine learning is required for this role.

Strong expertise in Bayesian statistics, including Gaussian processes and Bayesian clinical trial design, is required for this role.

Deep foundations in statistics, linear algebra, probability, and machine learning are rigorously applied in model development.

Python and PyTorch skills are applied to build and validate clinical AI models that support optimal treatment selection.

Experience with Bayesian statistics for uncertainty quantification and model explainability is required.

Deep learning experience, including self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability, and computational pathology, is a bonus.

A record of publications in top venues such as ICML, ICLR, NeurIPS, CVPR or top-tier statistics journals is preferred.

Practical notes

Leadership is earned through initiative and consistent delivery of exceptional results.

Strong work ethic and ruthless prioritization are expected in a fast-paced clinical AI environment.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role focuses on Bayesian methods for clinical prediction and uncertainty quantification.

Multi-modal AI research spans imaging, survival analysis, and causal inference.

Tools include probabilistic modeling frameworks and deep learning libraries.

The team collaborates closely with clinical experts and AI pioneers.

Research output and clinical impact are central to success in this position.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.