Lead Data Scientist Healthcare / GenAI / LLM

healthcare data science experience and demonstrated production-level Generative AI (GenAI) and Large Language Model (LLM) expertise. This is a true data science and technical leadership rolenot a BI, data, Strong technical foundation: A formal educational foundation in Computer Science, Data Science, Statistics, Machine Learning, or a closely related quantitative discipline.

• Healthcare data science experience: Recent, substantive experience applying data science and machine learning within healthcare.

• Advanced ML ownership: Demonstrated end-to-end ownership of sophisticated machine learning solutions, including model development, validation, deployment, monitoring, and productionization.

• Production GenAI / LLM expertise: Recent hands-on experience developing and deploying GenAI/LLM solutions in production, such as RAG, agentic AI, NLP/LLM applications, prompt or model evaluation, or comparable enterprise AI solutions. Candidates should have built and deployed these solutionsnot simply used or evaluated GenAI tools.

• Healthcare data expertise: Direct experience working with clinical, patient, EHR/EMR, claims, population health, or other complex healthcare datasets, with an understanding of the challenges associated with applying AI/ML in a regulated healthcare environment.

• Technical leadership: Demonstrated Lead-level technical leadership through mentoring or guiding other data scientists, influencing modeling and technical direction, partnering with senior stakeholders, and translating complex data science work into meaningful business or clinical outcomes. Candidates Who Do Not Meet the Requirements

• Candidates whose backgrounds are primarily ETL/data pipelines, data engineering, BI/reporting, supply chain, or operations research.

• Candidates focused primarily on traditional analytics without substantial advanced data science and machine learning experience.

• Traditional ML candidates without meaningful, hands-on production GenAI/LLM experience.

• GenAI/LLM candidates without substantive healthcare data science experience.

• Candidates who have only experimented with GenAI tools or have exposure to LLMs without demonstrating production development and deployment., Required QualificationsEducation

• Bachelors degree or higher in Science, Engineering, Computer Science, Mathematics, Statistics, or another related STEM discipline.
• Masters degree in Data Science is preferred.

Professional Experience

• Minimum of seven (7) years of professional experience in data science.
• Experience within a hospital environment, medical informatics, healthcare information technology, healthcare finance/revenue cycle data, or Electronic Health Record (EHR) data management is preferred.

Technical & Analytical Expertise

• Advanced statistical analysis, including regression, statistical testing, probability/distribution concepts, and appropriate application of statistical methodologies.
• Machine learning methodologies, including clustering, decision trees, artificial neural networks, and other predictive modeling approaches, with an understanding of their practical strengths and limitations.
• Data science methodologies such as time-series forecasting, linear regression, A/B testing, statistical testing, clustering, predictive analytics, and related techniques.
• Advanced SQL and database management.
• Programming and statistical analysis tools used within modern data science environments.
• Data modeling, algorithm development, data mining, visualization, and pattern analysis.
• Business analytics, including process analysis, workflow development, spreadsheets, modeling, and related analytical techniques.
• Data architecture and design principles.
• Advanced problem solving, analytical reasoning, troubleshooting, and decision-making.
• Identifying, investigating, and resolving complex data quality and integrity issues.
• Working with large, complex, incomplete, and unstructured data sources.

Communication & Business Skills

• Ability to gather business requirements and convert analytical findings into clear, meaningful business insights.
• Strong data storytelling skills and the ability to explain complex analytical results to both technical and non-technical audiences.
• Excellent written and verbal communication skills within both IT and business environments.
• Ability to present findings and recommendations to senior leadership, including A-C level executives.
• Strong customer service orientation and ability to produce high-quality work products.
• Ability to manage challenging stakeholder and client situations.
• Ability to translate complex technical information into practical recommendations for a broad range of stakeholders.

Project & Leadership Skills

• Advanced understanding of the complete data science project lifecycle.
• Demonstrated ability to independently manage multiple projects with competing priorities.
• Strong project management skills and consistent ability to meet objectives, deadlines, and deliverables.
• Comfortable working with minimal supervision in a fast-paced, multidisciplinary environment.
• Ability to lead cross-functional teams and complex analytical initiatives.
• Ability to provide technical guidance, coaching, and mentoring to other data scientists and less experienced staff.

The primary screening objective is to identify candidates who clearly demonstrate the intersection of all four of the following areas:

• Healthcare Data Science
• Advanced Data Science / Machine Learning
• Production GenAI / LLMs
• Technical Leadership

Candidates should clearly demonstrate all four areas on their resume. submissions should prioritize candidates whose experience provides specific evidence of hands-on technical work, production deployments, healthcare data experience, and Lead-level technical leadership.

The Houston Chronicle named us a Top Workplace for the 10th consecutive year. They partnered with Energage to gather survey responses from employees, and ranked companies based on criteria ranging from company values and culture, to leadership and benefits.

We rated highly based on our values and the meaningful work we do. Were honored to receive this recognition and inspired to do more for our people.

Nigel