Senior Manager, Advanced Analytics Products

Sysco

18d

About The Position

The Senior Manager, Advanced Analytics Products leads the design, development, scaling, and adoption of advanced digital analytics solutions—translating business needs into robust technical requirements and ensuring models and products deliver measurable outcomes. This leader partners closely with Data Science, business SMEs, and technology teams to co-develop solutions, validate outputs, and convert complex analytics into clear, actionable recommendations. In addition to product execution, the role drives the AI/analytics strategy, designs experiments and proofs-of-concept, collaborates on solution architecture for ML/LLM initiatives, and mentors the analytics community to foster innovation.

Requirements

Education: Bachelor’s degree in an analytical or quantitative field (e.g., Engineering, Mathematics, Business, Economics) or in Mathematics, Statistics, Computer Science, or equivalent self-study.
Experience: 10 years of professional experience of hands-on visualization design and development in large-scale and complex environments.
Leadership: 5 years in a management role leading data/analytics teams, plus 10 years of hands-on visualization design/development in large-scale, complex environments.
Technical: Proficiency with SQL; experience with Tableau and Tableau Server .
Translate business questions into technical requirements and interpret analytics outputs; familiarity with descriptive, predictive, and prescriptive analytics, including AI/ML concepts.
Excellent communication skills; adept at engaging business users and technical partners; highly organized with the ability to manage multiple initiatives in parallel.
Understanding of data governance, quality, and privacy considerations.
Comfort interfacing with both technical and management teams; ability to work in a collaborative, cross-functional environment and problem-solve using a structured, logical framework.

Nice To Haves

Familiarity with LLMs and generative AI applications; exposure to ML Ops, model lifecycle management, and prompt engineering.
Hands-on experience designing and running experimentation; strong knowledge of BI tools and data visualization best practices; demonstrated success driving enterprise adoption of AI solutions.