Senior AI Data Scientist — Generative & Production ML

Baylor Scott & White Health; Atlanta (GA); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Health benefits

401(k) match

Tuition Reimbursement

PTO

Job summary

Baylor Scott & White Health in Atlanta is seeking a Senior Data Scientist, AI to design and deploy ML models, generative AI, and agentic workflows that power high-impact clinical and operational use cases.

You will collaborate with product, engineering, data, and clinical teams to prototype, validate, and productionize data science assets, ensuring interpretable outputs, robust monitoring, and measurable outcomes.

Qualifications

• 5–8+ years of experience in data science, ML, or analytics.
• Experience developing models and analytical assets that influence product, operational, clinical, or business outcomes.
• Hands-on experience building and deploying LLM-powered or agentic capabilities with enterprise data.
• Experience working with cross-functional teams and governing stakeholders.

Responsibilities

• Translate AI use-case objectives into data science problem statements and plans.
• Develop predictive models, segmentation, personalization, and statistical analyses.
• Prototype, validate, and productionalize data science, generative AI, and agentic methods.
• Collaborate with product, engineering, data, ontology, and clinical stakeholders.

Skills

Python

SQL

Statistics

Machine learning

Generative AI

LLM/Agentic AI

Data visualization

Experiment design

Education

Bachelor's degree in Data Science/Statistics/CS

Master's degree preferred

Tools

Pandas

NumPy

Scikit-learn

Statsmodels

PyTorch

TensorFlow

Spark

Databricks

Snowflake

Job description

Baylor Scott & White Health in Atlanta is seeking a Senior Data Scientist, AI to design and deploy ML models, generative AI, and agentic workflows that power high-impact clinical and operational use cases.

You will collaborate with product, engineering, data, and clinical teams to prototype, validate, and productionize data science assets, ensuring interpretable outputs, robust monitoring, and measurable outcomes.