Senior Data Scientist

Leidos

About The Position

Leidos is seeking a Senior Data Scientist to work closely with client stakeholders, supporting economic analysis of national importance through the application of advanced statistics and data science techniques and technologies. In this position, you will utilize your strong background in statistics, machine learning, generative AI, visualization, economic analysis, and big data processing to plan and execute projects that meet business client data needs. Leidos is a Fortune 500® technology, engineering, and science solutions and services leader, working to solve the world’s toughest challenges in the defense, intelligence, civil, and health markets. The Leidos Civil Group helps the government modernize operations with leading-edge AI/ML driven data management and analytics solutions, serving as a trusted partner to government and highly-regulated commercial customers like the FAA, DOE, DOJ, NASA, National Science Foundation, Transportation Security Administration, Custom and Border Protection, airports, and electric utilities to make the world safer, healthier, and more efficient.

Requirements

Bachelors degree with 8+ years of applied data science experience or a Master’s degree with 6+ years of prior relevant experience.
Mastery of Python or R, statistical tools such as Stata, SAS and strong SQL.
Expertise with ML algorithms (e.g. model selection, evaluation, feature engineering, etc.)
Expertise with application of AI to deliver insights with optimal performance, cost savings, etc. using structured, unstructured data.
Expertise with data visualization tools (e.g. Tableau, Matplotlib).
Experience processing large datasets, deploying LLMs in government cloud data platforms (e.g. Azure ADLS/Databricks/Azure ML, or equivalents).
Comparative understanding of LLMs (e.g. Claude Code, ChatGPT), and tradeoffs in terms of capabilities, cost, and performance.
Excellent problem-solving skills and the ability to think critically and analytically to address complex research challenges.
Exceptional verbal and written communication skills and a bias toward rigor, clarity, and defensibility over black-box modeling are essential.
Familiarity with FISMA/NIST/Zero Trust security frameworks
Current Principal Data Scientist (PDS) Certification.

Nice To Haves

Experience with Spark/Databricks.
PhD preferred in Statistics, Mathematics, or a related quantitative field.
Domain exposure to antitrust, pricing, healthcare claims, fraud/forensics, or financial analysis is a plus.
Experience producing reproducible, peer-reviewed-method analyses that meet Rule 702/Daubert reliability requirements and can withstand Daubert challenges (methods, error rates, standards/controls, and appropriate bounds on conclusions)
Contribution to open-source projects or participation in relevant data science communities.

Responsibilities

Partner with stakeholders to scope questions and assumptions, support solution design, and facilitate project execution.
Design and implement robust data ingestion, storage, integration, processing, retrieval, and management strategies for research datasets.
Build transparent, reproducible pipelines and analysis environments in cloud infrastructures.
Produce crisp exhibits and memos that explain methods, limitations, and uncertainty.
Facilitate and execute data-driven research by applying sophisticated statistical, machine learning, and computational methods to analyze complex datasets related to computer and information science.
Create compelling data visualizations and reports that convey complex research findings in a clear and accessible manner to both technical and non-technical stakeholders.
Support defensible analytics and econometric/causal inference workstreams, translating ambiguous business or legal questions into testable hypotheses and clear, client-ready findings.
Design and execute rigorous studies (e.g., difference-in-differences, panel models with fixed/random effects, instrumental variables/2SLS, time series/forecasting, etc.) to turn multi-source datasets into documented, auditable results.
Mentor teammates on best practices.
Stay updated on the latest academic research and industry advancements in data science, AI, and information systems, and apply relevant findings to ongoing projects.