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Postdoctoral In Bayesian Statistics Jobs (NOW HIRING)

ChromoLogic

Senior Scientist, Bioinformatics

Monrovia, CA · On-site

$90K - $120K/yr

Apply advanced machine learning, Bayesian statistics, and predictive modeling techniques to ... D. in Computational Biology, Bioinformatics, Biostatistics, Statistics, Computer Science, or a ...

Exponent

Statistical Scientist (Ph.D.)

Bellevue, WA · On-site

D. in Statistics, Biostatistics, or a related field • Excellent communication skills and the ... Bayesian statistics, machine learning, or quality control/improvement • Minimum of 2 years ...

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$59K

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How much do postdoctoral in bayesian statistics jobs pay per year?

As of Aug 24, 2026, the average yearly pay for postdoctoral in bayesian statistics in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What is a postdoctoral position in Bayesian statistics?

A Postdoctoral position in Bayesian Statistics is a research-focused role for individuals who have recently completed their PhD in statistics, mathematics, or a related field. These positions involve conducting advanced research using Bayesian methods, which apply probability to infer statistical conclusions. Postdocs often work on developing new Bayesian models, collaborating on interdisciplinary projects, and publishing research findings. Such positions are typically temporary and designed to further prepare researchers for academic, industry, or governmental roles.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in Bayesian statistics?

To thrive as a Postdoctoral Researcher in Bayesian Statistics, you need an advanced degree (typically a PhD) in statistics or a related field, with strong expertise in Bayesian inference and probabilistic modeling. Proficiency with statistical programming languages such as R, Python, or Stan, and experience with specialized Bayesian analysis software are highly valued. Excellent problem-solving skills, collaboration, and the ability to communicate complex statistical concepts clearly are standout soft skills for this role. These skills and qualities are crucial for conducting rigorous research, publishing impactful results, and contributing effectively to scientific teams.

What are some common challenges faced by postdoctoral researchers in Bayesian statistics, and how can they be addressed?

Postdoctoral researchers in Bayesian statistics often encounter challenges such as managing complex, high-dimensional data, staying current with rapidly evolving computational methods, and balancing independent research with collaborative projects. Effective strategies include leveraging open-source statistical software, actively participating in seminars and workshops to stay updated, and establishing regular communication with interdisciplinary teams. Building a strong professional network and seeking mentorship within the department can also help in navigating research obstacles and advancing one's career.

What is the difference between Postdoctoral In Bayesian Statistics vs Postdoctoral In Data Science?

AspectPostdoctoral In Bayesian StatisticsPostdoctoral In Data Science
Required CredentialsPhD in Statistics, Mathematics, or related fieldPhD in Computer Science, Statistics, or related field
Work EnvironmentAcademic research, university labsResearch institutions, tech companies, industry labs
Employer & Industry UsageUniversities, research institutesTech firms, finance, healthcare, consulting
Common Search & Comparison IntentSpecialized research roles in Bayesian methodsBroader data analysis and machine learning roles

Postdoctoral In Bayesian Statistics focuses on advanced research in Bayesian methods within academic settings, requiring deep statistical expertise. In contrast, Postdoctoral In Data Science covers a broader range of data analysis techniques, including machine learning, often in industry environments. Both roles require a PhD but differ in application focus and work environment.

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Postdoctoral In Bayesian Statistics jobs near you
Infographic showing various Postdoctoral In Bayesian Statistics job openings in the United States as of August 2026, with employment types broken down into 79% Full Time, 19% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

Senior/Staff Machine Learning Data Scientist

Bayesian Health

Remote

Full-time

Re-posted 5 days ago

Job description

Senior/Staff Machine Learning Data Scientist
In Brief
  • We're an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives.
  • TLDR: Independent end-to-end model development with ability to work cross-functionally with Clinical, Engineering, and Product to clarify and prioritize specifications and features for our life-saving AI models.
Who We Are
Bayesian Health's mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We're a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data.
We're funded by top tier tech and biotech investors: Andreessen Horowitz, American Medical Association's venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year.
Read more about our recent publication in Nature Medicine that associates our products with lives saved.
What you'll do
As a Senior/Staff Machine Learning Data Scientist, you are not satisfied with training and tuning ML models that predict clinical conditions in patients; you also want to own the effectiveness of your model in the real world. In practice, that means you aren't afraid to get your hands dirty by writing data mapping code, debugging a specific patient case by following patient data as it moves through our AWS services, or improving the timeliness of your model's predictions by reading and writing production-grade Python and SQL code.
Responsibilities
  • Model Prototyping: Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods
  • Productionizing: The same models that you develop with production-grade Python
  • Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploy production-grade Python code to implement those strategies
  • Cross-Functional Alignment: Data Science for storytelling - understand model performance and metrics, and present this to technical and non-technical users, both internally and externally
Minimum qualifications
  • Ph.D. in a relevant field plus 3+ years experience shipping ML based software products
  • Experience owning your ML models from prototyping to production, especially real-time algorithms that update dynamically across time
  • Experience writing production-grade Python and SQL code to implement and evaluate ML models in production systems
  • Track record of using statistics and performance metrics to compare end-to-end ML and product performance

Preferred qualifications
  • Experience shipping breakthrough or 0-1 products from end to end, interpreting and leveraging State-of-the-Art methods to do so
  • Experience using messy clinical and health data to design new products for large Health Systems
  • Experience with any of the following: PyTorch, PySpark, HL7, FHIR, EHR, time series data, signal processing, MLFlow, anomaly detection, Bayesian statistics, quantile regression, time-series forecasting
  • You bring passion and enthusiasm to your work, and are excited to join a growing team to Get Stuff Done and save lives!

Bayesian Health provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

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