Director Statistical Programming Jobs in Virginia

This Department of War enterprise data and analytics program delivers mission-critical capabilities that enable leaders across the Department to make faster, better-informed decisions using trusted data at scale. Leidos Digital Modernization sector is seeking an experienced Senior Data Scientist Lead to support the delivery, enhancement, and adoption of enterprise data and analytics products used across multiple DoD organizations.

In this role, you will work alongside government partners, engineers, and other industry teammates to translate operational and strategic requirements into scalable, production-ready solutions. You will contribute directly to product planning, execution, and continuous improvement-helping ensure capabilities are delivered efficiently, aligned to mission priorities, and positioned for sustained success.

This position offers the opportunity to work on a high-visibility, enterprise program at the intersection of data, analytics, and emerging AI technologies. Ideal candidates are motivated by mission impact, comfortable operating in complex stakeholder environments, and interested in building deep domain expertise while delivering capabilities with real-world national security outcomes.

Primary Responsibilities:

• Lead efforts to extract insights from operational, service, and performance data to identify opportunities for improvement.

• Lead development and deployment of advanced statistical models, machine learning algorithms, and predictive analytics solutions.

• Design and develop predictive models and data-driven analytical frameworks that optimize processes and support informed decision-making.

• Build models that forecast future demands, highlight operational and service-related risks, and detect performance anomalies in real time.

• Collaborate with engineering and functional teams to ensure analytical outputs are accurate, actionable, and aligned with mission objectives.

• Design experiments, feature engineering strategies, and model validation frameworks to support enterprise analytics objectives.

• Collaborate with data engineering teams to ensure scalable data pipelines supporting model training and inference.

• Integrate models into DevSecOps pipelines for automated testing, validation, and production deployment.

• Develop and maintain documentation, evaluation metrics, and model performance dashboards.

• Ensure responsible AI practices including bias detection, explainability, and performance monitoring.

• Participate in PI Planning, backlog refinement, sprint reviews, and Inspect & Adapt events to align analytics priorities with Program Increment (PI) objectives.

• Translate complex analytical findings into actionable insights for technical and executive stakeholders.

• Provide direct supervision, mentoring, and performance management for assigned data scientists and analytics personnel.

• Conduct performance evaluations, goal setting, and professional development planning.

• Lead workforce planning and staffing alignment across data science workstreams.

• Establish modeling standards, peer review processes, and analytical quality governance frameworks.

• Foster a collaborative, innovative, and mission-focused analytics culture within the organization.

Basic Qualifications:

• Active Top Secret (TS) clearance with SCI eligibility.

• Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, or related technical discipline and 8 years of relevant experience OR Master's degree in a related field and 6 years of relevant experience.

• Minimum of 8 years of experience in data science, data engineering, or a related field.

• Strong proficiency in programming languages such as Python, R, SQL or similar analytical programming languages.

• Experience with data engineering tools and platforms, such as Hadoop, Spark, or similar.

• Experience developing and deploying machine learning and statistical models in enterprise environments.

• Proven experience in designing and developing predictive models and data-driven analytical frameworks.

• Experience performing data exploration, feature engineering, model validation, and performance tuning.

• Knowledge of data security policies, including data encryption and access controls.

• Experience with data governance frameworks and compliance enforcement.

• Strong analytical and problem-solving skills.

• Excellent communication and collaboration skills.

• Demonstrated experience leading and mentoring technical analytics teams.

Preferred Qualifications:

• Active TS/SCI clearance.

• Experience operating within SAFe or large-scale Agile frameworks supporting enterprise systems.

• Experience supporting analytics initiatives across NIPRNet, SIPRNet, and JWICS environments.

• Experience operationalizing AI/ML models in cloud-native environments (AWS, Azure, or GCP).

• Knowledge of machine learning algorithms and frameworks such as PyTorch, TensorFlow, Scikit-learn, or equivalent.

• Experience implementing model monitoring, drift detection, and continuous validation frameworks.

• Experience supporting enterprise data, AI/ML, or digital modernization programs within DoD environments.

• Experience with cloud-based data platforms and tools, such as AWS, Azure, or Google Cloud.

• Experience with data visualization tools, such as Tableau or Power BI.

• Experience with data catalog management and data sharing agreements.

• Strong project management skills and experience leading cross-functional teams.

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares.

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.