Planetary Protection Data Jobs

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4d ago

Spaceflight Data Scientist

Leidos Houston, TX

Houston, TX

4d ago

$108k - $195k/yr Full time Bayesian Modeling Data Analysis Data Analytics

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Job Description

Leidos invites applications for a Spaceflight Data Scientist for NASA JSC, onsite in Houston. This role emphasizes Bayesian decision theory and normative decision analysis to support Mars sample return and planetary protection. Travel may be required.

Benefits and culture

Competitive compensation
Health and Wellness programs
Income Protection
Paid Leave
Retirement

Location: Houston, TX (onsite). Salary: USD 107,900 - 195,050 per year.

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.

Requirements

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

Technologies

Stan
R
Python

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