Journeyman Data Scientist supporting delivery of enterprise data and analytics solutions. Collaborate with teams to deliver scalable, production-ready solutions in a mission-driven environment.
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.
Requirements
Active Secret clearance
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
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