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.

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.