Data Science Manager - Space Planning Intelligence

Position Purpose

We are transforming how merchandising decisions are made through AI-powered solutions and automation. The Data Science Manager – Space Planning Intelligence plays a critical role in the technical design and delivery of AI-enabled planogram automation capabilities. This role partners closely with Strategy and Process teams to ingest business requirements and translate them into a scalable technical roadmap, overseeing the development of business rules, AI/ML algorithms, and optimization techniques that accurately reflect merchandising intent.

As the technical owner of the solution, this leader designs the end-to-end technical strategy to integrate tools and platforms together into a cohesive, scalable process, ensuring the underlying math, automation logic, and learning systems are sound, efficient, and adaptable.

As a people and technical leader, the Data Science Manager leads and mentors a team of data scientists and solution engineers, setting coding standards, QA processes, and deployment best practices to ensure solutions are robust, maintainable, and production-ready. Acting as a trusted technical advisor to Strategy and business partners, this role evaluates proposed initiatives for technical feasibility, identifies advanced analytical and AI opportunities, and translates high-level business goals into impactful automated systems.

The role also collaborates closely with fellow Data Science Managers to align approaches, manage talent effectively, and deliver clear, executive-level communication that drives alignment across all levels of the organization.

• 40% People Management & Team Leadership
  - Provide leadership, mentorship and coaching to Data Scientists (individual contributors) and evaluate performance, execution, and contribution;
  Attract, retain and develop top talent to build an effective and high performing team;
  Support team in development of business knowledge and technical skills with clear plans and regular assessment of progress;
  Ensure appropriate processes and job-specific requirements are properly documented;
  Develop onboarding and training plan for both technical and soft skills that will ensure high level of capabilities within the team;
  Manage resources effectively to align with strategic initiatives and business needs;
  Foster collaboration with team members to develop a supportive and engaged team;
  Support team in the design and development of algorithms and models to use against large datasets to create business insights;
  Drive team's results and celebrate accomplishments.
• 25% Project Management
  - Prioritize and assign projects by striking a balance between a variety of factors, like funding, return on investment, innovative or challenging work, among others;
  Manage programs for the team to ensure consistency of execution, balance of resources and quality solutions;
  Collaborate with program managers and business partners to align on objectives and expectations and ensure project teams deliver against them;
  Serve as escalation point for any issues or difficulties with specific projects;
  Provide executive updates to leaders at all levels of the organization on program effectiveness, team accomplishments, summary of findings and recommended solution, and explain internal and external impacts.
• 15% Strategy & Business Development
  - Leverage extensive business knowledge and relationships to seek out new business opportunities to leverage data science and advocate for team's skills;
  Effectively develop trust and collaboration with internal customers and cross-functional teams;
  Ensure alignment with IT support model, collaboration, tools and governance to enable data scientists to be most effective in executing their roles;
  Collaborate with other data science managers across the organization to establish high level strategy and optimize talent management.
• 20% Technical Exploration & Development
  - Seek further knowledge on key developments within data science, technical skill sets, and additional data sources;
  Participate in the continuous improvement of data science and analytics by developing replicable solutions (for example, codified data…