Healthcare Data Science Advisor: AI & Analytics Leader

Cigna Health and Life Insurance Company; Austin, Northern (TX, KY); Hybrid; USD 108,000 - 180,000; Full time

Benefits offered by this job

Health benefits

401(k)

Life insurance

Tuition reimbursement

Paid time off

Paid holidays

Job summary

The Cigna Group is seeking a Data Science Advisor in Austin to lead enterprise analytics using AI and ML. You will collaborate with clinical, operations, technology, and business leaders to deliver impactful insights and scalable solutions, shaping how data science is used across Evernorth Health Services.

You will build predictive models, develop data pipelines, and present practical recommendations to diverse audiences while maintaining high quality and innovative standards.

Qualifications

• Relevant experience in data science, advanced analytics or related field.
• Experience with predictive modeling and various ML techniques.
• Ability to explain complex findings to non-technical audiences.

Responsibilities

• Lead design, development, and implementation of predictive and AI analytics solutions.
• Build and improve supervised and unsupervised models using diverse methods.
• Develop scalable data pipelines and analytics workflows on large healthcare datasets.
• Turn complex findings into clear insights and actionable recommendations.
• Partner with clinical, operations, technology, and business teams to deliver projects.
• Provide thought leadership on advanced analytics and responsible AI approaches.

Skills

Python

R

SQL

SAS

Data visualization

Communication

Collaboration

Education

Master's degree

PhD

Tools

scikit-learn

MLlib

TensorFlow

PyTorch

Job description

The Cigna Group is seeking a Data Science Advisor in Austin to lead enterprise analytics using AI and ML. You will collaborate with clinical, operations, technology, and business leaders to deliver impactful insights and scalable solutions, shaping how data science is used across Evernorth Health Services.

You will build predictive models, develop data pipelines, and present practical recommendations to diverse audiences while maintaining high quality and innovative standards.