Data Scientist

Here at Baylor Scott & White Health we promote the well-being of all individuals, families, and communities. Baylor Scott and White is the largest not-for-profit healthcare system in Texas that empowers you to live well.

• We serve faithfully by doing what's right with a joyful heart.
• We never settle by constantly striving for better.
• We are in it together by supporting one another and those we serve.
• We make an impact by taking initiative and delivering exceptional experience.

• Immediate eligibility for health and welfare benefits
• 401(k) savings plan with dollar-for-dollar match up to 5%
• Tuition Reimbursement
• PTO accrual beginning Day 1

Note:

Benefits may vary based on position type and level.

Work Authorization Requirement: Candidates must be legally authorized to work in the United States without employer sponsorship. Visa sponsorship is not available for this position at this time.

Care Coverage Analytics supports the data science and advanced analytics needs of Baylor Scott & White Health and the Baylor Scott & White Quality Alliance (ACO). We support over 1.2 million lives across multiple blocks of business by partnering with operational teams and subject-matter experts to improve healthcare decisions and outcomes.

We are seeking a Data Scientist 1 to develop practical data science, natural language query (NLQ), and agentic AI solutions for complex healthcare problems. Serving as both a data scientist and a consultative partner, you will work with data scientists, data engineers, subject-matter experts, and business partners to frame problems, challenge assumptions, and turn questions into reliable models and AI-enabled solutions that improve access to trusted information, reduce manual work, and support better decisions.

In this role, you will contribute across the solution lifecycle, from problem definition and data preparation through development, evaluation, deployment, and monitoring. You will study how people use NLQ and agentic products and help turn behavioral patterns, feedback, and unmet needs into product improvements.

You will also use AI-assisted coding tools and learn to package repeatable work as reusable agent skills and internal AI solutions. This applied role requires curiosity, sound data science foundations, and a commitment to accuracy, security, explainability, and responsible AI.

Communication and Consulting | 25%:
Build trusted relationships with business, clinical, and technical partners. Clarify needs, frame problems, challenge assumptions, recommend appropriate analytical approaches, establish success measures, and communicate findings, tradeoffs, and limitations in language that supports decisions and action.

NLQ and Agentic AI | 20%: Develop and improve grounded NLQ applications, data science copilots, reusable agent skills, and multi-step AI workflows for healthcare questions and recurring technical work. Contribute to internal tools that accelerate development and test whether outputs are accurate, useful, and reproducible.

Applied Data Science and Experimentation | 20%: Apply statistical analysis, machine learning, natural language processing, behavioral segmentation, and other appropriate techniques to healthcare and product questions. Establish baselines, analyze user journeys and cohorts, design experiments, compare solution options, document assumptions, and communicate limitations.

Model and Agent Lifecycle | 15%: Support models and AI agents from experimentation through deployment and monitoring. Use validation checks, interaction patterns, feedback, task outcomes, and error analysis to monitor adoption and performance drift and improve reliability, explainability, security, and user experience.

Data Preparation and Feature Development | 10%: Use SQL and Python to explore, prepare, and validate claims, EHR, member, quality, and operational data. Develop reproducible modeling datasets and features, assess data quality and bias, and use governed metrics, semantic models, metadata, and business definitions to ground NLQ and agentic solutions in consistent information.

Continuous Learning and Knowledge Sharing | 10%: Participate in peer reviews, document methods, seek feedback, share knowledge with teammates, and stay current with developments in data science, generative AI, agentic systems, healthcare AI, and responsible AI.

• Consult with business, clinical, and technical partners to…