Sr. Manager AI and Data Science

Job Summary

The Senior Manager, AI and Data Science provides leadership for a multidisciplinary data science team that builds advanced analytics solutions using emerging Artificial Intelligence (AI) and Machine Learning (ML) technologies to enhance healthcare operations, patient care, and overall health plan performance and efficiency.

This role provides strategic direction, technical oversight, and people leadership for multiple work streams ranging from traditional predictive modeling to Agentic AI, Large Language Models (LLMs), and Natural Language Processing (NLP) solutions.

The leader will also be a key driver of enterprise AI adoption by enabling the team to use Agentic AI frameworks to automate and accelerate day‑to‑day analytical and engineering tasks while ensuring appropriate governance, privacy, and responsible AI practices.

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 to 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 requiring 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 related field required.

• Master's degree or PhD preferred.

• Equivalent years of relevant work experience may be accepted.

• Six (6) years of experience in predictive analytics, data science, or a related field, preferably within the healthcare industry or managed care organizations.

• Three (3) years of leadership experience.

• One (1) year of experience with cloud services (such as Azure, AWS or GCP) and modern data stack (such as Databricks or Snowflake).

• One…