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We take great strides to ensure our employees have the resources to live well, be healthy, continue learning, develop skills, grow professionally and serve our local communities. We invite you to apply for a career with Blue Cross. Residency in or relocation to Louisiana is preferred for all positions. What This Team Does The Healthcare Analytics & Data Science team develops data-driven solutions that strengthen how healthcare is delivered, valued, and understood. Using machine learning, statistical modeling, and generative AI, the team analyzes data, finds key correlations, and recommends strategies to improve patient outcomes, provider performance, risk-adjusted revenue, and organizational initiatives that support cost-effective care. This team partners closely with business units and IT to deliver predictive models, prescriptive insights, and innovative AI based tools that improve decision-making and operational efficiency across the enterprise. As a Healthcare Data Science Intern, you’ll support data exploration, model development, analytic problem-solving, and the creation of AI/ML solutions that advance organizational performance and member outcomes.
Responsibilities
Assist in developing statistical, predictive, and machine learning models related to patient utilization, outcomes, and provider performance.
Contribute to generative AI initiatives that enhance internal processes, automate insights, or improve user engagement.
Work with large datasets to perform data wrangling, feature engineering, and exploratory data analysis.
Partner with business stakeholders to translate analytical findings into actionable insights.
Collaborate with IT teams to support model deployment efforts and analytic tool development.
Document modeling methods, results, and assumptions in a clear and professional manner.
Contribute to projects focused on medical cost optimization, risk adjustment, and engagement analytics.
Requirements
Currently pursuing one of the following: Data Science, Computer Science, Statistics, Mathematics, Economics, Analytics, or other quantitative/health care related field. Senior undergraduate, recent graduate, or graduate student
Coursework in machine learning, statistics, data analysis, or programming is preferred
GPA Preferred: 3.5+ Minimum: 3.0 cumulative GPA
Working knowledge of Python for data analysis (e.g., pandas, NumPy) and comfort working in notebook-based environments (such as Databricks or Jupyter notebooks).
Foundational SQL skills for querying, joining, and transforming data.
Understanding of core statistics and data science concepts (e.g., descriptive statistics, regression or classification, model evaluation fundamentals).
Introductory experience with machine learning concepts and Python‑based ML libraries (e.g., scikit‑learn), through coursework, projects, or internships.
Experience using Microsoft Excel and PowerPoint to analyze data and communicate results.
Strong analytical thinking skills with the ability to interpret data, solve problems, and clearly summarize findings for non-technical audiences.
Effective written and verbal communication skills and ability to work collaboratively on a team.
Nice-to-haves
Exposure to Databricks platform and scalable data processing concepts (e.g., PySpark, Delta tables).
Familiarity with BI and data visualization tools, especially Power BI or Tableau, including dashboard design best practices.
Additional experience with advanced ML or AI frameworks (e.g., PyTorch, TensorFlow) or LLM/GenAI tooling (e.g., LangChain, OpenAI APIs) through projects or experimentation.
Awareness of modern data platforms or lakehouse architectures (e.g., curated vs. raw data, reproducible analytics).
Interest in learning how analytics connects to internal tools or lightweight applications, with optional exposure to APIs, VS Code, or basic front-end concepts over time.
Curiosity and a proactive approach to learning new tools and technologies
Strong attention to detail and organization
Comfort working with large datasets and complex technical concepts
Ability to collaborate with cross functional teams, including business and IT functional teams
Interest in healthcare analytics, machine learning, and AI driven problem-solving
