Sr. Manager AI and Data Science

• Lead, coach, and develop a data science and AI team, including hiring, goal setting, performance development, and fostering a culture of quality, learning, and responsible innovation.
• Stay current on the latest trends in LLMs, NLP, generative AI, and healthcare informatics, integrating relevant advancements into projects to drive continuous innovation.
• Develop and implement predictive models, ML algorithms, and statistical techniques identify patterns, trends, and opportunities for improving operational efficiency, cost containment, and patient care from large and complex healthcare datasets.
• Oversee development of predictive models using claims, EHR/EMR, lab, utilization management, and other data sources; ensure appropriate evaluation, calibration, drift monitoring, and clinical interpretability.
• Conduct rigorous data analysis, including data cleansing, feature engineering, and exploratory data analysis, to derive meaningful insights and actionable recommendations.
• Develop, test, and deploy generative AI solutions (including prompt engineering and RAG) with appropriate documentation, monitoring, and safeguards.
• Drive team adoption of Agentic AI frameworks to accelerate day-to-day work (e.g., requirements drafting, code generation, test creation, data quality checks, documentation, and analysis automation), while setting standards for safe tool use and human-in-the-loop review.
• Manage a technical sub-team focused on NLP and deep learning techniques that process and analyze unstructured healthcare data (e.g., clinical notes, patient feedback, and medical literature) to extract meaningful insights.
• Partner with architecture and data solutions teams to move solutions from prototype to production; co-own operational readiness including documentation, observability, incident response, and post-release tuning for both ML and GenAI systems.
• Implement LLMOps/MLOps practices: reproducible pipelines, experiment tracking, model and prompt versioning, automated evaluation, and monitoring/alerting aligned with business and clinical risk.
• Ensure compliance with HIPAA/PHI handling and internal governance standards; collaborate with privacy, security, and compliance partners to mitigate risks such as data leakage, prompt injection, and unsafe outputs.
• Collaborate with cross-functional stakeholders (clinical leadership, risk adjustment, care management, operations, IT, and analytics partners) to prioritize work, define KPIs, and communicate results, tradeoffs, and roadmap progress.
• Represent the department in forums that require clinical AI and generative AI expertise; communicate complex concepts clearly to technical and non-technical audiences.
• Perform any other job related duties as requested.

• Bachelor's degree in Data Science, Mathematics, Statistics, Engineering, Computer Science, or other related field required
• Master's degree or PhD preferred
• Equivalent years of relevant work experience may be accepted in lieu of required education
• Six (6) years of experience in predictive analytics, data science, or a related field, preferably within the healthcare industry or managed care organizations required
• Three (3) years of leadership experience required
• One (1) year of experience with cloud services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflakes) required
• One (1) year of experience delivering LLM and/or generative AI solutions (e.g., prompt engineering, fine-tuning approaches, and/or RAG) from prototype through production required

• People leadership and organizational skills, including the ability to hire, coach, and retain high-performing technical talent; set clear priorities; and build an inclusive, collaborative team culture
• Strong expertise in statistical modeling, machine learning, and predictive analytics using Python and/or R
• Familiarity with agentic AI, LLM architectures, prompt engineering, RAG, and evaluation metrics
• Familiarity with transformer architectures and modern deep learning frameworks; familiarity with generative modeling concepts and tradeoffs (quality, latency, and cost)
• Knowledge of RAG design patterns, including document processing, chunking strategies, embeddings, retrieval (keyword/semantic/hybrid), reranking, and context management
• Knowledge of evaluation methods for LLM/RAG systems (retrieval metrics, groundedness/faithfulness, answer quality, and bias/safety checks) and the ability to operationalize automated evaluation and regression testing
• Familiarity with MLOps/LLMOps practices, including CI/CD, experiment tracking, model and prompt versioning, automated testing/evaluations, monitoring/observability, and reproducible pipelines
• Expertise in Optical Character Recognition (OCR) technologies, including data extraction from scanned documents, forms, and invoices, and proficiency in OCR tools and libraries
• Understanding of AI governance and responsible AI, including privacy-by-design, HIPAA/PHI handling, model risk management, and safeguards against prompt injection and data leakage
• Proficiency in SQL and working knowledge of data modeling, data quality, and feature engineering for large healthcare datasets
• Knowledge of healthcare operations, payer and provider models, and industry trends
• Proficient in feature engineering techniques and exploratory data analysis
• Excellent analytical, problem-solving, and critical-thinking skills, including the ability to translate complex data into actionable insights
• Strong project/program leadership skills, including the ability to lead multiple initiatives simultaneously, manage dependencies, and deliver measurable outcomes
• Excellent written and verbal communication and presentation skills, including the ability to convey technical concepts to non-technical stakeholders
• Knowledge of health care coding including CPT-4, HCPCS, ICD-9/10, DRG and Revenue Codes required
• Knowledge of managed care required
• Comfortable reading academic research papers and applying them in the models

• None

• General office environment; may be required to sit or stand for extended periods of time
• Ability to travel as required by the needs of the business.

Compensation Range:

CareSource takes into consideration a combination of a candidate’s education, training, and experience as well as the position’s scope and complexity, the discretion and latitude required for the role, and other external and internal data when establishing a salary level. In addition to base compensation, you may qualify for a bonus tied to company and individual performance. We are highly invested in every employee’s total well-being and offer a substantial and comprehensive total rewards package.

Compensation Type (hourly/salary):

Organization Level Competencies

• Fostering a Collaborative Workplace Culture

• Cultivate Partnerships

• Develop Self and Others

• Drive Execution

• Influence Others

• Pursue Personal Excellence

• Understand the Business

This job description is not all inclusive. CareSource reserves the right to amend this job description at any time. CareSource is an Equal Opportunity Employer. We are dedicated to fostering an environment of belonging that welcomes and supports individuals of all backgrounds.