1

Bayesian Statistics Jobs in Texas (NOW HIRING)

... RBD, Bayesian) and RAS metrics to Data Center architectures. Proficiency with reliability software (e.g., Weibull++, BlockSim, JMP, Minitab) and scripting languages for statistical modeling like ...

Experience with statistical methods such as Bayesian methods to identify trends and anomalies in multivariate and/or medium-sized and/or large-scale datasets. Experience with human anthropometric ...

Data Center - MLB Reliability Engineer

Austin, TX · On-site

$101K - $127K/yr

... RBD, Bayesian) and RAS metrics to Data Center architectures. Proficiency with reliability software (e.g., Weibull++, BlockSim, JMP, Minitab) and scripting languages for statistical modeling like ...

Experience with statistical methods such as Bayesian methods to identify trends and anomalies in multivariate and/or medium-sized and/or large-scale datasets. Experience with human anthropometric ...

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Use machine learning and statistical modelling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis and others to develop and evaluate algorithms for improving ...

Demographic Data Scientist

Austin, TX · On-site

$85 - $110/hr

Experience with statistical methods such as Bayesian methods to identify trends and anomalies in multivariate and/or medium‑sized and/or large‑scale datasets. * Experience with human ...

Showing results 41-60

Bayesian Statistics information

See Texas salary details

$4

$13

How much do bayesian statistics jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for bayesian statistics in Texas is $13.23, according to ZipRecruiter salary data. Most workers in this role earn between $13.22 and $13.22 per hour, depending on experience, location, and employer.

What is Bayesian statistics?

A Bayesian Statistics job involves using Bayesian methods to analyze data, update probabilities, and make inferences based on prior knowledge. Professionals in this field apply Bayesian techniques in areas like machine learning, finance, healthcare, and scientific research. They typically work with probabilistic models, statistical software, and programming languages such as Python or R. These roles require strong mathematical skills and are often found in academia, industry, and government research.

What does a typical day look like for someone working in Bayesian statistics?

A typical day for a professional specializing in Bayesian Statistics often involves designing and running statistical models, analyzing datasets using Bayesian methods, and programming in tools like R or Python. You may collaborate with data scientists, researchers, and subject matter experts to define problems and interpret statistical results. Responsibilities can also include presenting findings to non-technical stakeholders, developing new modeling techniques, and staying updated with advances in Bayesian methodology. The role offers a dynamic mix of technical analysis, problem-solving, and teamwork, making each day intellectually engaging.

What are the key skills and qualifications needed to thrive in the Bayesian statistics position, and why are they important?

To thrive in Bayesian Statistics, you need a deep understanding of probability theory, statistical modeling, and strong programming skills, usually supported by an advanced degree in statistics, mathematics, or a related field. Familiarity with technical tools like R, Python, Stan, and software for Bayesian inference, as well as relevant certifications, is often required. Analytical thinking, attention to detail, and the ability to clearly communicate complex concepts are essential soft skills. These skills and qualities ensure accurate and interpretable statistical analyses, effective collaboration with cross-functional teams, and reliable data-driven decision making.

What can you do with Bayesian statistics?

A professional in Bayesian statistics applies probabilistic models to analyze data, make predictions, and update beliefs based on new information. This skill is used in fields like data science, machine learning, and research to improve decision-making and model uncertainty. Proficiency in statistical software such as R or Python is often required.

What is a Bayesian statistician?

A Bayesian statistician is a professional who applies Bayesian methods to analyze data, update probabilities, and make statistical inferences. They often use tools like statistical software and require strong knowledge of probability theory and modeling techniques to interpret data within a Bayesian framework.

What jobs use Bayesian statistics?

Jobs that use Bayesian statistics include data scientists, statisticians, machine learning engineers, and quantitative analysts. These roles often involve developing probabilistic models, analyzing data, and making predictions using Bayesian methods and tools like R or Python. Strong analytical skills and knowledge of statistical software are typically required.

What are popular job titles related to Bayesian Statistics jobs in Texas?

For Bayesian Statistics jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Bayesian Statistics jobs in Texas look for?

The top searched job categories for Bayesian Statistics jobs in Texas are:

Infographic showing various Bayesian Statistics job openings in Texas as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $27,518 per year, or $13.2 per hour.

Spaceflight Data Scientist (NASA HHPC)

Via Logic LLC

Houston, TX • On-site

$107.90 - $195.05/hr

Other

Posted 17 days ago


Job description

On the Human Health and Performance Contract we are at the forefront of safeguarding astronaut health, ensuring human performance, and advancing technologies for human space exploration. We are seeking a Spaceflight Data Scientist with deep expertise in Bayesian inference, Bayesian decision theory, and normative decision theory to contribute to NASA's Mars Backwards Planetary Protection efforts to support the Statistics and Data Science team for the Biomedical Research and Environmental Sciences division at the NASA Johnson Space Center (JSC) in Houston, TX.

What You'll Do:

In this highly specialized role, you will build rigorous, mathematically grounded frameworks that inform critical decisions regarding contamination risk, biosafety protocols, evidence-evaluation strategies, and risk-management tradeoffs for Mars sample return and human/robotic exploration. Your work will directly influence policies and operational strategies that protect Earth's biosphere while enabling scientific advancement. You will help define the decision frameworks that safeguard Earth during one of the most significant scientific endeavors of our time. Your contributions will shape how humanity explores Mars, evaluates extraterrestrial samples, and manages biosafety risks across multiple mission architectures. This is a rare opportunity to apply high-level decision science to challenges at the frontier of planetary exploration, astrobiology, and human spaceflight.

This position is 100% on site and in person. Occasional travel may be required.

Primary Responsibilities:
  • Develop, implement, and compare Bayesian decision-theoretic and normative decision-analytic models to guide contamination-risk and policy decisions for Mars sample return and backwards planetary protection.
  • Construct probabilistic models that integrate microbial, environmental, and operational uncertainties across Mars-to-Earth transfer pathways.
  • Determine optimal testing, containment and mitigation strategies by quantifying tradeoffs among evidence, stakeholder values, mission constraints, and risk tolerances.
  • Conduct formal uncertainty quantification, sensitivity analysis, and scenario exploration grounded in advanced decision theory.
  • Clearly communicate assumptions, decision logic, analytic results, and policy implications to interdisciplinary teams, mission planners, and regulatory partners.
  • Contribute to compliance documentation and ensure analytical rigor in support of NASA's protective mandates.
Required Education and Experience:
  • Master's Degree and 10 years of experience or Ph.D. and 5 years of experience in Decision Sciences, Statistics, Health Economics, Operations Research, or a related quantitative discipline.
  • Demonstrated expertise in Bayesian inference, Bayesian decision theory , and normative decision theory , including eliciting priors, utilities, and multi-attribute value structures.
  • Proficiency with probabilistic programming tools such as Stan, R, Python , or equivalent.
  • Strong foundation in experimental design, probabilistic modeling, and formal risk-assessment methodologies.
  • Exceptional analytical rigor and quantitative problem-solving abilities.
  • Ability to communicate complex technical concepts to diverse audiences.
  • Collaborative mindset suited for interdisciplinary research and operational teams.
  • Strong attention to detail, particularly in documentation and model governance.
Desired Skills:
  • Experience applying decision theory to planetary protection , astrobiology , microbial risk assessment , or high-stakes biosafety domains.
  • Familiarity with COSPAR Planetary Protection guidelines and NASA risk-based decision processes.
  • Background in modeling for Mars Sample Return or similar exploration missions with stringent biosafety considerations.
  • Prior support of NASA programs or spaceflight research.

Must be able to obtain a Public Trust Clearance; due to contract requirements, U.S. Citizenship or U.S. Permanent residency is required.

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares.

Original Posting:

July 24, 2026

For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

Pay Range $107,900.00 - $195,050.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment wi

#J-18808-Ljbffr