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Probabilistic Programming Bayesian Jobs in Houston, TX

... Bayesian decision theory , and normative decision theory , including eliciting priors, utilities, and multi-attribute value structures. * Proficiency with probabilistic programming tools such as Stan ...

... Bayesian decision theory , and normative decision theory , including eliciting priors, utilities, and multi-attribute value structures. * Proficiency with probabilistic programming tools such as Stan ...

Probabilistic Programming Bayesian information

See Houston, TX salary details

$146.6K

$267.5K

$328.5K

How much do probabilistic programming bayesian jobs pay per year?

As of Aug 8, 2026, the average yearly pay for probabilistic programming bayesian in Houston, TX is $267,533.00, according to ZipRecruiter salary data. Most workers in this role earn between $248,800.00 and $308,000.00 per year, depending on experience, location, and employer.

What are the typical challenges faced by professionals working in probabilistic programming with a Bayesian focus, and how can they be addressed?

Professionals working in Probabilistic Programming with a Bayesian focus often encounter challenges related to model complexity, computational efficiency, and communicating results to non-technical stakeholders. Building accurate Bayesian models requires careful selection of priors and an understanding of underlying data distributions, which can be demanding without robust domain expertise. Additionally, computational demands can be high, especially for large datasets or complex hierarchical models, making efficient sampling and approximation methods essential. Collaborating closely with domain experts and leveraging modern probabilistic programming frameworks can help address these challenges and ensure practical, interpretable results.

What is probabilistic programming in the context of Bayesian statistics?

Probabilistic programming in the context of Bayesian statistics refers to writing computer programs that use probability distributions and Bayesian inference to model uncertainty and learn from data. These programs allow users to define complex probabilistic models using code, making it easier to specify, fit, and analyze Bayesian models. Probabilistic programming languages, such as Stan, PyMC, or Edward, provide tools to automate inference, enabling practitioners to focus on modeling rather than mathematical derivations. This approach is widely used in fields like machine learning, data science, and scientific research to handle uncertainty and make predictions.

What is the difference between Probabilistic Programming Bayesian vs Data Scientist?

AspectProbabilistic Programming BayesianData Scientist
Required credentialsBackground in statistics, probability, programmingStatistics, computer science, or related degree
Work environmentResearch, modeling, algorithm developmentData analysis, visualization, business insights
Industry usageAI, machine learning, research projectsBusiness, finance, tech, healthcare

Probabilistic Programming Bayesian focuses on developing models using Bayesian methods and probabilistic programming languages, often in research or AI development. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require statistical knowledge, Bayesian programmers specialize in probabilistic modeling, whereas Data Scientists apply a broader set of data analysis techniques.

What are the key skills and qualifications needed to thrive as a probabilistic programming Bayesian specialist?

To thrive as a Probabilistic Programming Bayesian specialist, you need a strong background in statistics, probability theory, and Bayesian inference, often supported by a degree in mathematics, statistics, computer science, or a related field. Expertise with probabilistic programming languages (such as Stan, PyMC, or TensorFlow Probability) and familiarity with statistical modeling software are also essential. Analytical thinking, problem-solving, and effective communication skills help translate complex models into actionable insights and collaborate with interdisciplinary teams. These skills and qualities are crucial for developing robust, interpretable models that inform decision-making in research and industry applications.
What are popular job titles related to Probabilistic Programming Bayesian jobs in Houston, TX? For Probabilistic Programming Bayesian jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Probabilistic Programming Bayesian jobs in Houston, TX look for? The top searched job categories for Probabilistic Programming Bayesian jobs in Houston, TX are:
Infographic showing various Probabilistic Programming Bayesian job openings in Houston, TX as of July 2026, with employment types broken down into 13% As Needed, 33% Full Time, 7% Part Time, 25% Temporary, 20% Nights, and 2% Summer. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $267,533 per year, or $128.6 per hour.

Spaceflight Data Scientist (NASA HHPC)

Leidos

Houston, TX

$107K - $195K/yr

Full-time

Posted 14 days ago


Leidos rating

8.3

Company rating: 8.3 out of 10

Based on 151 frontline employees who took The Breakroom Quiz

77th of 483 rated business services


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 onlyand 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 with market data, applicable bargaining agreement (if any), or other law.


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About Leidos

Sourced by ZipRecruiter

At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers' success. We empower our teams, contribute to our communities, and operate sustainable practices. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community.

Industry

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Company size

10,000+ Employees

Headquarters location

Reston, VA, US

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