Director, Data Science

MCG Health•Seattle, WA

1d•$177,300 - $248,200•Hybrid

About The Position

The Director, Data Science provides strategic and operational leadership for MCG's Data Science team responsible for developing advanced AI solutions that improve clinical decision support for payers and providers. This role leads a team of senior and staff-level data scientists in designing, developing, and deploying production-grade AI systems that leverage foundation models, proprietary clinical evidence, and emerging AI technologies to deliver measurable customer value. Working closely with Product, Engineering, Clinical, and executive leadership, the Director establishes technical direction, aligns AI research with business priorities, and ensures successful delivery of scalable AI capabilities. This leader balances technical depth with people leadership, fostering a high-performing, collaborative team while advancing MCG's AI strategy through innovation, operational excellence, and responsible adoption of emerging technologies.

Requirements

Master's degree or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field, or equivalent practical experience.
At least 10 years of experience developing machine learning, natural language processing, or AI solutions, including production deployment of large-scale systems.
Demonstrated experience leading teams of senior and staff-level data scientists or machine learning engineers.
Demonstrated success delivering AI-enabled products or services from concept through production.
Deep understanding of large language model architectures, prompt engineering, retrieval-augmented generation (RAG), and model evaluation methodologies.
Experience with fine-tuning techniques and modern approaches for adapting foundation models.
Experience developing AI solutions in cloud environments such as AWS, Azure, or Google Cloud Platform.
Strong proficiency in Python and modern machine learning frameworks such as PyTorch.
Experience building scalable distributed AI systems and production ML pipelines.
Excellent written and verbal communication skills with the ability to communicate complex technical concepts to diverse audiences.

Nice To Haves

Experience developing AI solutions within healthcare or other highly regulated industries preferred.
Experience with advanced agentic AI systems, autonomous workflows, and reasoning agents preferred.
Experience with Kubernetes, Databricks, Flyte, or comparable cloud-native data science platforms preferred.
Familiarity with retrieval-augmented generation (RAG), vector databases, and enterprise AI architectures preferred.
Demonstrated ability to influence technical strategy across cross-functional teams and organizational boundaries.
Strong analytical, strategic thinking, and problem-solving skills.
Experience using AI-assisted software development tools (e.g., Claude Code, Cursor, GitHub Copilot, or similar) to improve engineering productivity and software quality preferred.

Responsibilities

Lead, coach, and develop a team of senior and staff data scientists, establishing clear priorities, fostering technical excellence, and supporting career growth.
Define departmental priorities, technical direction, and execution plans that align AI initiatives with business objectives, product roadmaps, and customer needs.
Ensure successful delivery of production-ready AI models, workflows, and services that meet quality, scalability, security, and performance expectations.
Partner closely with Product Management, Engineering, Clinical, and Infrastructure teams to prioritize work, resolve dependencies, and deliver customer-focused AI solutions.
Prototype and evaluate novel AI techniques, architectures, and agentic workflows that advance MCG's clinical AI capabilities and create competitive differentiation.
Provide technical leadership in the design, evaluation, fine-tuning, and deployment of large language models and related AI technologies.
Establish engineering and scientific best practices for experimentation, model evaluation, reproducibility, documentation, and operational excellence.
Monitor emerging AI technologies, research, and industry trends to identify opportunities for innovation and continuous improvement.
Communicate technical strategy, risks, tradeoffs, and progress to senior leadership and cross-functional stakeholders.
Manage departmental planning, resource allocation, and execution to ensure delivery against organizational priorities.
Foster a collaborative, inclusive, and high-performing team culture built on continuous learning and innovation.
Champion the effective and responsible use of AI-assisted development tools and workflows across the team to improve productivity, quality, and delivery.