Data Science / Decision Science Intern

Leidos•Reston, VA

1d•Remote

About The Position

The Health Sector at Leidos is seeking a Data Science / Decision Science Intern (Health Data Intelligence) to support advanced analytics initiatives in a remote, U.S.-based environment. This role targets high-performing graduate-level candidates with demonstrated experience applying data science to real-world, decision-driven problems. You will contribute to the development of scalable data products, reporting ecosystems, and decision-support frameworks built on standardized data sources. The role emphasizes applying advanced analytics, visualization, and emerging AI/ML techniques to generate a decision advantage across clinical, operational, and strategic domains. This internship is structured as a full-time summer experience (12–16 weeks) with the potential to extend into a part-time role during the academic year.

Requirements

Bachelor’s degree in a quantitative discipline (e.g., Data Science, Statistics, Mathematics, Computer Science, Engineering, or related field)
Currently pursuing a graduate degree (Master’s or Ph.D. candidate) in a relevant quantitative or analytical field (required)
Demonstrated, hands-on experience with Python and SQL for data analysis and manipulation
Experience working with healthcare data (clinical, claims, public health, or operational datasets)
Experience working in Jupyter notebooks or equivalent analytical environments
Proven experience developing data visualizations and dashboards using Tableau, Power BI, Looker, or similar tools
Demonstrated end-to-end project experience, including data ingestion, analysis, modeling, and presentation of results
Strong foundation in statistics, data analysis, and structured problem-solving
Experience applying machine learning techniques (e.g., regression, classification, clustering, or similar methods) in academic, research, or project settings
Familiarity with AI-assisted analytical workflows (e.g., use of generative AI or automation to enhance coding, analysis, or insight generation)
Ability to work independently on complex, ambiguous analytical problems and deliver high-quality outputs
Strong written and verbal communication skills, including the ability to present findings to technical and non-technical audiences
U.S. Citizenship preferred

Nice To Haves

Prior internship, research, or project experience developing production-grade data products, analytical pipelines, or reporting systems
Experience applying analytics in a decision science, business intelligence, or strategy-oriented context
Experience working with cloud-based data platforms (e.g., AWS, Azure, GCP, Snowflake)
Familiarity with data engineering concepts, including ETL pipelines, data modeling, and data standardization
Familiarity with data governance, metadata management, or data quality frameworks
Demonstrated ability to create executive-level dashboards and presentations that drive decision-making
Portfolio, GitHub repository, or equivalent body of work demonstrating applied data science, analytics, or visualization projects
Evidence of intellectual curiosity, initiative, and creativity in applying data to solve complex, real-world problems

Responsibilities

Perform data extraction, transformation, and preparation using Python, SQL, and standardized data sources
Conduct exploratory data analysis (EDA) to identify trends, patterns, and data quality issues
Apply statistical analysis and machine learning techniques (e.g., regression, classification, clustering) to generate insights and support decision-making
Develop and operationalize scalable reporting frameworks and reusable data products aligned to standardized data models
Design and deliver advanced dashboards and visualization solutions using Tableau, Power BI, Looker, or similar platforms
Translate analytical outputs into decision-ready insights, including structured recommendations and trade-off analysis
Collaborate with stakeholders to define requirements and deliver production-oriented analytical solutions
Identify opportunities to improve data quality, standardization, and analytical efficiency
Communicate findings through executive-ready visualizations, storytelling, and concise written deliverables