Sr. Director of Data Science Engineering

Health Care Service Corporation•Chicago, IL

10d•$161,500 - $299,700•Hybrid

About The Position

At HCSC, our employees are the cornerstone of our business and the foundation to our success. We empower employees with curated development plans that foster growth and promote rewarding, fulfilling careers. Join HCSC and be part of a purpose-driven company that will invest in your professional development. Job Summary HCSC is recruiting for a leader in our Data Science Engineering function. The team is responsible for the engineering practices that bring AI and machine learning solutions to production at scale, including model deployment, MLOps, monitoring, and reliability. The Senior Director will lead a team of machine learning engineers who partner closely with data scientists, product managers, and IT to operationalize AI/ML solutions that deliver tangible business value. This role is situated within the Data Science & AI Solutions team at HCSC, the central hub of data science work within the organization. Join our team of driven, fun, and accomplished builders, and help us advance HCSC’s mission: to do everything in our power to support our members, in sickness and in health. Please note, this is a HYBRID position and will require 3 days/week in-office hours at the respective office location.

Requirements

Bachelor’s degree and 7 years of work experience in a computer science, engineering, or related field OR Master’s degree and 6 years of work experience in a computer science, engineering, or related field OR Ph.D. and 4 years of work experience in a computer science, engineering, or related field
4 years management or leadership experience.
Learning and growth mindset.
Customer-focused.
Interpersonal, verbal and written communication skills.
Must demonstrate proficiency in at least five and mastery in one of the following six areas: 1) software engineering and version control practices 2) machine learning model development and evaluation 3) MLOps, CI/CD, and model deployment 4) a high-level programming language (e.g., Python) 5) generative and agentic AI 6) understanding of healthcare.
Iterative and agile development practices.
Independently delivering or leading the delivery of machine learning engineering solutions for multiple complex analytics or data science projects and products.
Overseeing the annual budget and allocating resources for various projects and operational needs.
Translating needs and initiatives into compelling business cases.
Conducting cost-benefit analyses to justify investments and ensure ROI.

Nice To Haves

Master’s degree in a computational field, or Bachelor’s degree with significant healthcare experience
Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native ML services
Experience with containerization (Docker, Kubernetes) and infrastructure-as-code
Experience with MLOps tooling (e.g., MLflow, Kubeflow, SageMaker, Azure ML, Databricks)
Experience deploying and operating generative AI / LLM-based solutions, including LLMOps, RAG architectures, and vector databases
Experience with model monitoring, drift detection, and responsible AI tooling

Responsibilities

Develop and implement strategic plans for the Data Science Engineering function, aligning with the organization’s objectives.
Lead a team of machine learning engineers to design, build, and operationalize scalable AI/ML solutions in collaboration with data scientists, product managers, and business stakeholders.
Establish and evolve MLOps practices, including CI/CD pipelines, model deployment, monitoring, and lifecycle management for both traditional ML and generative AI systems.
Architect and oversee scalable, secure, and reliable infrastructure for model training, inference, and serving on cloud platforms.
Drive engineering best practices include code quality, automated testing, observability, reproducibility, and responsible AI principles.
Partner with IT, platform, and data engineering teams to integrate AI/ML solutions into enterprise systems and workflows.
Ensure policies and procedures align with the corporate vision, regulatory requirements, and industry best practices.
Foster effective communication and collaboration within the team, stakeholders, and management.
Recruit, develop, and evaluate personnel to ensure the efficient operation of the Data Science Engineering team.
Serve as a liaison and support effective communication and strategy between the team and stakeholders.
Oversee the budget and operations of the function, ensuring efficient resource allocation and alignment with goals.