Data Science / Decision Science Intern

General Program Information / Position Overview:

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.

Primary 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

Basic Qualifications

• 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

Preferred Qualifications

• 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, initi