Journeyman Data Scientist

Leidos•Alexandria, VA

22h•$87,100 - $157,450

About The Position

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 Journeyman Data Scientist 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.

Requirements

Active Top Secret (TS) clearance with SCI eligibility.
Bachelor’s degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, or related technical discipline and 4-8 years of relevant experience OR Master’s degree in a related field and 2-6 years of relevant experience.
Minimum of 4 years of experience in data science, data engineering, or a related field.
Experience applying statistical analysis and machine learning techniques.
Experience using programming languages such as Python, SQL, R or similar analytical programming languages.
Experience working with data analysis and ML libraries (e.g., Pandas, Scikit-learn, or similar).
Experience performing data exploration, feature engineering, and model validation.
Proven experience in designing and developing predictive models and data-driven analytical frameworks.
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.

Nice To Haves

Active TS/SCI clearance.
Experience operating within Agile or SAFe frameworks supporting enterprise systems.
Experience with data engineering tools and platforms, such as Hadoop, Spark, or similar.
Experience with data visualization tools, such as Tableau or Power BI.
Experience developing and deploying machine learning and statistical models in enterprise environments.
Experience supporting analytics across multi-enclave DoD environments.
Experience working with cloud-based data and AI/ML platforms (AWS, Azure, or GCP).
Experience with machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, or equivalent).
Familiarity with LLMs, generative AI, or NLP techniques.
Experience supporting AI/ML model deployment and operationalization.
Experience with data catalog management and data sharing agreements.
Experience implementing model monitoring, drift detection, and continuous validation frameworks.

Responsibilities

Develop and apply statistical models and machine learning algorithms to analyze structured and unstructured data.
Perform data exploration, feature engineering, and data preprocessing to support analytics and model development.
Support development and validation of predictive and descriptive analytics models.
Collaborate with data engineers to ensure availability and quality of data for analytics workflows.
Assist in integrating models into production environments through APIs and DevSecOps pipelines.
Develop visualizations, dashboards, and reports to communicate analytical findings.
Support evaluation of model performance, including accuracy, bias, and reliability.
Assist in implementation of model monitoring and continuous improvement processes.
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 cross-functional teams including AI/ML engineers, software developers, and domain stakeholders.
Ensure responsible AI practices including bias detection, explainability, and performance monitoring.
Translate complex analytical findings into actionable insights for technical and executive stakeholders.
Develop and maintain documentation, evaluation metrics, and model performance dashboards.
Document analytical methodologies, models, and results.
Participate in SAFe ceremonies including sprint planning, backlog refinement, sprint reviews, and retrospectives.