Senior Manager, Advanced Analytics Products

Position Summary 

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. 

Key Responsibilities 

Model & Product Development 

• Partner with Data Scientists to co-develop solution designs (POCs, iterations), embedding business context; provide feedback on model design, testing outputs, and data preparation (exploration, cleansing, integration). 

• Design experiments, evaluate objectives, and iterate on proofs-of-concept using multiple methodologies; collaborate on technical constraints and solution architecture for ML/LLM-based initiatives. 

• Guide the approach and model design for use cases tied to core enterprise analytics engines; ensure appropriate data elements are identified, acquired, and consolidated at scale. 

Insight Translation & Executive Communication 

• Convert complex model outputs into actionable insights through concise executive summaries, dashboards, visuals, and storyboards. 

• Identify the most relevant outputs to visualize and deliver for decision-making. 

Stakeholder Engagement & Change Adoption 

• Act as the liaison between business users and technical teams, aligning on needs, requirements, and outputs; run working sessions to explain results and capture feedback for iteration. 

• Drive adoption by integrating outputs into operational processes; work cross-functionally with functional teams (e.g., merchandising, revenue management, HR), operating companies, specialty companies/geographies, and technology teams to scale models and realize value. 

Use Case Framing & Intake 

• Identify and shape future analytics opportunities—defining high-impact projects and providing early input on technical dependencies, constraints, and feasibility. 

Operationalization & Scaling 

• Ensure models are effectively scaled; develop metrics and tracking to ensure value capture from deployed analytical models. 

Team Leadership & Capability Building 

• Mentor analytics professionals on emerging AI technologies and visualization best practices; manage associate and business partner resources to deliver analytics and dashboarding for prioritized use cases. 

Typical Time Allocation (guidance) 

• Model & Product Development Support: ~50% 

• Business Communication & Insight Translation: ~20% 

• Stakeholder Engagement & Change Adoption: ~20% 

• Use Case Framing & Intake Planning: ~10% 

Minimum Qualifications 

• 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

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Professional Skills 

• 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. 

Preferred Qualifications 

• 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. 

Reporting & Collaboration 

Collaborates closely with Data Science, Business Technology, and functional teams to ensure models are effectively designed, scaled, and embedded into core business processes for tangible value creation.