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Postdoctoral In Bayesian Statistics Jobs in Seattle, WA

Think Tank, Inc.

Statistician

Seattle, WA · On-site

Knowledge and experience in Bayesian statistics and mark-recapture methods is strongly preferred. * Advanced degree in a related field - may substitute for two (MS) or four (PhD) years of experience ...

Think Tank, Inc.

Statistician

Seattle, WA

Knowledge and experience in Bayesian statistics and mark-recapture methods is strongly preferred. * Advanced degree in a related field - may substitute for two (MS) or four (PhD) years of experience ...

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 ...

Exponent Inc.

Statistical Scientist (Ph.D.)

Bellevue, WA · On-site

$133K - $150K/yr

Thanks for your interest in joining our team! Key statistics: * 950+ Consultants * 640+ Ph.D.s * 90 ... Bayesian statistics, machine learning, or quality control/improvement * Minimum of 2 years ...

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Postdoctoral In Bayesian Statistics information

See Seattle, WA salary details

$28.5K

$67.2K

$95K

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

As of Jul 23, 2026, the average yearly pay for postdoctoral in bayesian statistics in Seattle, WA is $67,168.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,800.00 and $75,700.00 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 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.

What are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Bayesian Statistics, and why are they important?

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 popular job titles related to Postdoctoral In Bayesian Statistics jobs in Seattle, WA? For Postdoctoral In Bayesian Statistics jobs in Seattle, WA, the most frequently searched job titles are:
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Postdoctoral In Bayesian Statistics jobs near you
Statistician

Statistician

Think Tank, Inc.

Seattle, WA • On-site

Full-time

Posted 13 days ago

Job description

*Position is Subject to Contract Award

POSITION DESCRIPTION:

Description of Duties:

  • Provide statistical analysis to inform management decisions.
  • Statistically analyze spatial and temporal variability in processes experienced by migrating salmon in river systems, especially survival and migration patterns through hydropower systems and across life cycles.
  • Analyze and generate relationships between fish responses (density, growth, survival, migration rate) and stressors in particular life stages.
  • Develop novel probabilistic mathematical and simulation models representing complex ecological and behavioral processes with modern statistical methods.
  • Provide statistical support to other researchers during study design and analysis; organize and curate ecological datasets relevant to mark-recapture modeling.
  • Document and share reproducible workflows and analytical protocols; publish reports and scientific papers and present at regional/national meetings and conferences.
  • Assist with field collection of fish and environmental data as needed (may involve riding in vehicles and/or boats).

EDUCATION & EXPERIENCE:

Required:

  • Education: PhD from an accredited college/university with a major related to the task order, with emphasis in statistics, mathematics, fisheries, ecology, or the natural sciences. Must have a strong quantitative background with a solid foundation and extensive coursework in statistics and probability.
  • Experience: Ten (10) or more years of experience related to the task order, including familiarity with the species and habitats managed by NOAA Fisheries in the West Coast region.

Desired:

  • Knowledge and experience in Bayesian statistics and mark-recapture methods is strongly preferred.
  • Advanced degree in a related field - may substitute for two (MS) or four (PhD) years of experience

CERTIFICATIONS:

Required:

  • Valid U.S. driver's license - required and maintained throughout the period of performance.
  • Public trust suitability; background investigation cleared prior to beginning performance.
  • Government-required training to be completed within 5 business days of start: NOAA IT Security, NOAA Safety, Sexual Assault/Sexual Harassment Prevention & Response (NAM 1330-52.222-70(b)(6)), and Records Management 101.

RESPONSIBILITIES:

Required (Deliverables):

  • All analyses conducted with accepted methods and QA/QC.
  • Reproducible analytical workflows documented; statistical scripts and methodologies shared via open-science platforms (e.g., GitHub).
  • Analysis products archived accessibly; final data and models shared via public scientific repositories or interactive research dashboards.
  • Published scientific papers and reports; regular communication with team members.
  • Written status reports and other ad hoc communications; participation in field tasks as needed.

SKILLS:

Required:

  • Extensive experience in statistical modeling and data analysis; extensive experience conducting analyses and coding in R.
  • Experience with programs Stan and JAGS; experience with open-science concepts.
  • Strong computational skills, including ability to manipulate large environmental datasets within a command-line environment (e.g., Linux/shell scripting).
  • Familiarity interpreting or interfacing with C or C++ code within a scientific modeling context; familiarity with Google Suite and Microsoft Office.

Excellent verbal and written communication; experience writing reports and publishing peer-reviewed articles; able to work independently and on interdisciplinary teams