Data Scientist Intern- Master's Program (Hybrid: NJ, TX, or MO)

Cigna Group•Austin, NJ

3d•Hybrid

About The Position

Make an Impact with Data Science Bring your curiosity, technical skills, and bold ideas to Evernorth Health Services, a division of The Cigna Group. Our Data Science Center of Expertise internship is a foundational development opportunity for emerging data science professionals ready to apply advanced analytics to meaningful healthcare challenges. You will contribute to strategically important work, learn alongside compassionate experts, and explore how innovative solutions can improve health outcomes and business decisions. This is more than a summer project. Intern-developed solutions have evolved into enterprise capabilities, been presented to senior leadership, and, in several cases, contributed to patented innovations. You will also build relationships through professional development, networking, and direct engagement with senior data science leaders and clinical stakeholders who use analytics to shape strategic and operational decisions.

Requirements

Pursuing a master's degree or PhD in data science, statistics, applied mathematics, computer science, bioinformatics, computational linguistics, or another quantitative field.
At least 1 year of hands-on data science or analytics experience gained through coursework, research, internships, or professional projects.
Working knowledge of Python or R, probability and statistics, and data preparation, cleansing, and interpretation.
Experience developing or evaluating analytical methods such as regression, decision trees, clustering, machine learning, text mining, or natural language processing.
Ability to explain technical work clearly, collaborate across disciplines, and adapt based on feedback and new information.

Nice To Haves

Experience querying relational or distributed datasets using SQL, Hive SQL, Spark, PySpark, Scala, Hadoop, or HDFS.
Experience with visualization tools or libraries such as Tableau, Shiny, ggplot, or Matplotlib.
Demonstrated interest in healthcare analytics, responsible AI, experimentation, or translating research into practical solutions.

Responsibilities

Develop and evaluate predictive, machine learning, natural language processing, or generative AI solutions that address complex healthcare and business challenges.
Translate large, varied datasets, including claims, member information, call transcripts, surveys, and web logs, into insights that can improve member outcomes, experiences, and operational effectiveness.
Partner with data scientists, engineers, business leaders, and clinical stakeholders to define problems, test ideas, and turn research into scalable solutions.
Build clear, reproducible data pipelines, models, and visualizations using tools such as Python, R, SQL, Spark, or related technologies.
Communicate findings, tradeoffs, and recommendations to technical and nontechnical audiences, including opportunities to present work to senior leadership.
Apply a responsible, growth-oriented approach by seeking feedback, testing assumptions, documenting methods, and considering data quality, privacy, fairness, and model performance.