Data Scientist

Leidos is actively interviewing for a Data Scientist to join our team in Springfield, VA.

Job Summary

Senior-most technical position responsible for leading the design, development, deployment, and optimization of advanced data science solutions in support of government mission objectives. The Data Scientist serves as a recognized subject matter expert in statistical modeling, machine learning, artificial intelligence, data exploitation, and analytic tradecraft. This role requires on-site support at a government facility and close collaboration with mission operators, analysts, engineers, program leadership, and government stakeholders in secure environments.

Key Responsibilities

• Lead the development and operationalization of advanced data science, machine learning, and artificial intelligence solutions for mission applications.

• Design and implement statistical models, predictive analytics, forecasting methods, optimization models, and decision-support tools.

• Extract, transform, integrate, and analyze structured, semi-structured, and unstructured data from multiple government and mission-relevant sources.

• Develop and validate machine learning models for classification, regression, clustering, anomaly detection, ranking, recommendation, and pattern discovery.

• Architect end-to-end analytic workflows, including data ingestion, feature engineering, model training, testing, deployment, monitoring, and lifecycle management.

• Apply advanced quantitative methods to derive actionable insights from large, complex, and high-value datasets.

• Collaborate with domain experts, mission analysts, software engineers, and government personnel to translate mission needs into technical solutions.

• Evaluate model performance, quantify uncertainty, and ensure analytic validity, repeatability, and interpretability.

• Support data governance, security, compliance, and responsible AI practices within government mission environments.

• Prepare technical documentation, briefings, reports, white papers, and stakeholder presentations.

• Advise leadership on data science strategy, analytic methodology, capability gaps, and technology insertion opportunities.

Required Technical Skills

• Statistical modeling and inference

• Machine learning and artificial intelligence

• Predictive analytics and forecasting

• Data mining and pattern analysis

• Feature engineering and model evaluation

• Data wrangling, integration, and transformation

• Python, R, SQL, and scientific computing libraries

• Visualization and dashboarding tools

• Model deployment, monitoring, and lifecycle support

• Documentation, briefing development, and technical communication

Basic Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Physics, or a related quantitative field.

• Minimum of 12–15 years of relevant professional combined experience in data science, advanced analytics, machine learning, artificial intelligence, or statistical modeling.

• Demonstrated experience developing and deploying production-grade analytic or machine learning solutions in government, defense, intelligence, or other highly regulated environments.

• Deep knowledge of statistical inference, probability, experimental design, predictive modeling, and machine learning methods.

• Strong programming experience in Python, R, SQL, or similar analytic languages.

• Experience working with large, complex, and potentially disparate datasets in enterprise environments.

• Familiarity with model validation, explainability, bias assessment, and performance evaluation techniques.

• Strong written and verbal communication skills with the ability to explain complex technical findings to diverse audiences.

• Active TS/SCI with ability to be approved for a Poly

Preferred Qualifications

• Master’s degree or Ph.D. in a relevant quantitative or technical discipline.

• Experience supporting DoD, IC, DHS, civilian federal agencies, or other government mission environments.

• Experience with deep learning, natural language processing, computer vision, graph analytics, time series analysis, or reinforcement learning.

• Familiarity with cloud platforms, MLOps, DevSecOps, and containerized deployment environments.

• Experience with distributed computing, big data platforms (Spark/Databricks or similar), and workflow orchestration tools.

• Familiarity with emerging Artificial Intelligence (AI) technologies, tools, and methodologies, wi