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Clinical Data Scientist

About Us

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.

Our Core Values are:

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.

Benefits

Our benefits are designed to help you live well no matter where you are on your journey. For full details on coverage and eligibility, visit the Baylor Scott & White Benefits Hub to explore our offerings, which may include:

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 upon position type and/or level.

Salary

The pay range for this position is $90k annually (entry-level qualifications) - $140,462k annually (highly experienced). The specific rate will depend upon the successful candidate's specific qualifications and prior experience.

Job Summary

The Clinical Data Scientist will collaborate closely with research teams to address complex and high-impact clinical challenges. The ideal candidate will be well-versed in Python and AI/ML frameworks and skilled with generative AI tools, with familiarity in software engineering. This expertise will support clinical research, inform decision-making, and drive improvements in patient outcomes.

Essential Functions of the Role

Communication and Clinical Consulting:
Translate complex data science and machine learning concepts into clear, actionable insights for clinicians, researchers, and non-technical stakeholders. Present data-driven findings to support clinical decision-making, research initiatives, and operational improvements.
OpenAI and Generative AI Applications:
Design, develop, and deploy solutions leveraging large language models (LLMs), including OpenAI-based systems, to extract insights from unstructured clinical data. Build prompt-driven and programmatic pipelines for clinical text understanding, information extraction, summarization, and decision support. Ensure responsible and effective use of generative AI in healthcare and research settings.
Natural Language Processing and Generative AI:
Design and implement NLP pipelines leveraging transformer architectures (e.g., Clinical BERT, ModernBERT) and large language models (LLMs, including OpenAI-based systems) to process clinical notes, imaging reports, and other unstructured EHR data. Develop explainable AI solutions to support clinical interpretation and research.
Medical Imaging and Multimodal Data Analysis:
Apply machine learning techniques to imaging data (e.g., echocardiography, ECG, radiology reports) and integrate multimodal data sources to enhance disease detection, phenotyping, and predictive modeling.
Data Engineering and Pipeline Development:
Design and maintain scalable data pipelines for clinical and research data, integrating diverse sources such as EHR
Data Collection and Optimization:
Extract, clean, and analyze data from SQL-based systems (MS SQL Server) and cloud-based environments like Microsoft Azure.
Research and Innovation:
Contribute to clinical research initiatives, including study design, model development, validation, and manuscript preparation. Stay current with advancements in AI/ML, clinical informatics, and digital health to continuously improve methodologies.

Key Success Factors

Master's degree in a quantitative field like computer science, engineering, statistics, mathematics, economics, or a related field. Significant demonstrated experience in the role. PhD preferred.
3+ years of hands-on data scientist mathematical predictive modeling experience in a business environment or equivalent.
Proficiency in common language/tools for AI/ML – (e.g., Python/Pyspark, Keras, Tensorflow libraries, etc.).
Experience working in a cloud environment such as Azure, and their ML services.
Great social skills, like communication and partnership, are needed. This is due to interaction with analytics, intelligence, and cross-functional teams.
Know algorithms for advanced analytics, like binary classification, regression, Neural Networks, and Natural Language Processing.
Demonstrated knowledge of software engineering topics, including classes, functions, version control, CI/CD, and unit tests.
Technical expertise with many compute environments.
Experience working in EDW cloud technologies - Snowflake.
Proven experience in working with large datasets and relational databases (SQL).