Manager, Data Science

Inland Empire Health Plan•Rancho Cucamonga, CA

1d•Hybrid

About The Position

Find joy in serving others with IEHP! We welcome you to join us in “healing and inspiring the human spirit” and to pivot from a “job” opportunity to an authentic experience! The Manager, Data Science leads a team of data scientists, ML engineers, and AI specialists. This role requires a strong balance of technical expertise in advanced machine learning (including Generative AI), leadership experience, health care domain knowledge preferably in the managed care space, and the ability to align AI-driven solutions with business strategy. The Manager, Data Science enables the development of production-grade ML models, building GenAI applications (LLMs, RAG pipelines, prompt engineering, finetuning, multimodal models), and drives measurable business outcomes. Commitment to Quality: The IEHP Team is committed to incorporate IEHP’s Quality Program goals including, but not limited to, HEDIS, CAHPS, and NCQA Accreditation.

Requirements

At least ten (10) years of experience, which should include a minimum of seven (7) years of experience in data science & ML and at least five (5) years in a leadership/managerial role
Proven track record in leading AI/ML projects from conception to production
Experience with cloud AI platforms (Azure ML, GCP Vertex AI) and/or on-prem MLOps setups
Direct experience in Managed Care and Healthcare analytics, including claims data, care management, utilization, risk adjustment, member engagement, or related areas
Exposure to multimodal AI (text, image, audio, video) applications
Experience with Kubernetes, KServe, Ray, or MLflow for scaling AI workloads
Bachelor's Degree in Mathematics, Statistics, Computer Science, or related field from an accredited institution
Familiarity with data governance, model interpretability, bias mitigation, and AI ethics
Strong analysis and critical thinking skills. Excellent communication and interpersonal skills
Strong programming skills in Python, PyTorch/TensorFlow, LangChain, Hugging Face, or equivalent framework
Ability to manipulate large datasets and using database and general-purpose programming language (R, Python, JavaScript, or other big data frameworks, Java, SQL)
Demonstrable ability to quickly understand new concepts all the way down to the theorems, and to come out with original solutions to mathematical issues
Ability to multi-task while maintaining careful attention to detail. Ability to handle multiple projects, data input, and strong problem-solving capability. Independent self-starter who is driven to success, takes great pride in accomplishments and works with a sense of urgency to meet deadlines and address competing priorities
Expertise in Generative AI (LLMs, transformers, embeddings, vector databases, RAG, fine-tuning, and prompt engineering). Strong business acumen and ability to translate technical solutions into executive-level impact stories.

Nice To Haves

Master's Degree in Mathematics, Statistics, Computer Science, or related field from an accredited institution preferred
Familiarity with basic principles of distributed computing and/or distributed databases
Knowledge of one or more business/functional areas. Working knowledge of diagnosis and procedure coding, medical terminology, knowledge of managed care, claims payment processes, and insurance terminology required

Responsibilities

Lead and mentor a team of data scientists, ML engineers, and AI specialists.
Define and execute the roadmap for AI/ML and Generative AI initiatives across the enterprise.
Partner with business and technology stakeholders to identify AI use cases that create measurable value.
Advocate for responsible AI practices, ensuring solutions are ethical, explainable, secure, and compliant.
Oversee development of advanced ML models and AI systems (predictive, prescriptive, and generative).
Design and implement GenAI solutions, including LLM fine-tuning, embeddings, retrieval-augmented generation (RAG), and prompt optimization.
Drive end-to-end MLOps practices: model training, evaluation, deployment, monitoring, and lifecycle management.
Ensure scalability and performance of AI solutions within enterprise data platforms.
Collaborate with data engineering to optimize data pipelines for AI workloads.
Stay at the forefront of GenAI, multimodal AI, and emerging ML techniques to evaluate theirrelevance and application.
Foster a culture of experimentation, rapid prototyping, and “fail-fast and pivot” approaches.
Hire, train, and manage support staff, while monitoring and evaluating outcomes. Conduct performance reviews of each team Member within IEHP guidelines.
Perform any other duties as required to ensure Health Plan operations and department business needs are successful.