Data Scientist – Expert

Leidos•Springfield, VA

16h•$116,350 - $210,325•Onsite

About The Position

Leidos is actively interviewing for a Data Scientist – Expert 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 Expert Level 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.

Requirements

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

Nice To Haves

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, with an understanding of their applications, implications, and integration into daily geospatial and intelligence workflows.
Knowledge of data architecture, data engineering, and enterprise analytics ecosystems.
Experience briefing senior government leadership and mission stakeholders.

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.