Data Science Manager. Irvine LilyLifestyle

Data Science Manager

This is an exciting opportunity for a blended role for Manager, Data Science within the Data Product space. The Manager 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 combines strong experience developing scalable solutions with zero‑to‑one product launches, moving concepts through to production.

The 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. They are comfortable working with modern data architectures, integrating data pipelines, and managing the full lifecycle of models and AI applications. They possess expertise in machine learning, statistical modeling, and data visualization to translate business needs into actionable insights.

They 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. The 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.

• 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 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, and governance frameworks for models and implementation strategies.
• Communicate model behavior and results into actionable insights for 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.

• 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 managing embeddings, vector indexing, and chunking of unstructured…