Data Science Manager

CorVel Career Site•Irvine, CA

6d•Remote

About The Position

This is an exciting opportunity for a blended role for Manager, Data Science within our Data Product space. The Manager Data Science is responsible for architecting, building, and deploying production-grade machine learning models, AI solutions, and data products while leading a team of advanced data professionals. This is a highly technical, hands-on role that requires a combination of strong experience developing scalable solutions and zero to one product launch moving them from concept through production. This role requires hands-on technical contribution and is not solely an oversight function. The ideal candidate has demonstrated experience building data products and operationalizing machine learning within a cloud environment such as Azure, AWS, or similar platforms. This role requires comfort working with modern data architectures, integrating data pipelines, and managing the full lifecycle of models and AI applications. This individual has expertise in machine learning, statistical modeling, and data visualization to translate business needs into actionable insights. You will guide a team in applying best practices for model assessment, feature engineering, and scalable deployment while collaborating across business functions to ensure alignment with organizational goals. The position works closely with key business stakeholders and development teams and operates within the full software development lifecycle to deliver secure, reliable, and maintainable solutions. This individual is expected to contribute directly to technical design, development, and deployment efforts while ensuring best practices across architecture, performance, and model lifecycle management. This position is open to remote or hybrid.

Requirements

Strong technical expertise in machine learning and AI solution development
Experience building and deploying data products in production environments
Hands-on experience with cloud platforms such as Azure, AWS , or similar ecosystems
Familiarity with ML lifecycle management, data pipeline orchestration, and production support
Solid understanding of software engineering principles and development workflows
Ability to write, review, and optimize Python and SQL code
Deep understanding of Large Language Model (LLM) orchestration, specifically how to build "Agents" that can reason, use tools, and execute workflows autonomously
Knowledge of Retrieval-Augmented Generation (RAG) patterns, including how to manage embeddings, vector indexing, and the "chunking" of unstructured data for AI consumption
Excellent written and verbal communication skills; ability to translate complex models to a business users and demonstrate value
Excellent presentation skills
Bachelor’s or Master’s degree in a relevant field (e.g., Data Science, Information Systems, Computer Science, Business, or related discipline). Master’s degree preferred
7+ years of hands-on experience in data science, ideally in a data product capacity in banking/finance, insurance or a related field
5+ years of demonstrated experience leading teams and driving enterpriseâlevel change initiatives

Responsibilities

Design, build, train, and deploy production-grade machine learning models, AI solutions and data products
Collaborate with business stakeholders to identify high impact opportunities and deliver data driven solutions that support strategic goals
Design and implement data visualization solutions to effectively communicate complex insights to business stakeholders.
Translate business requirements into actionable data science projects aligned with organizational strategy.
Collaborate cross-functionally to identify data sources, ensure data quality, and deploy scalable models into production environments.
Develop a framework to monitor model performance post-deployment and recommend adjustments to maintain accuracy and robustness.
Stay ahead of industry trends, emerging technologies, and best practices in data science and AI
Partner with engineering teams to build and support data pipelines using technologies such as Azure Data Factory or comparable tools
Work closely with development teams across the full SDLC , ensuring solutions are production-ready, secure, and maintainable
Play a key role in architectural decisions, technical design reviews, governance frameworks for models and implementation strategies
Communicate model behavior and results into actionable insights to business and technical stakeholders
Manage a team of highly technical data and AI professionals to complete project deliverables and provide guidance on modeling techniques, feature engineering, and data analysis methods
Foster a high-performing team culture through coaching, mentoring, and professional development
Additional duties as assigned