Data Science Manager - Space Intelligence (Remote)

Home Depot•Atlanta, GA

4d•Remote

About The Position

We are transforming how merchandising decisions are made through AI-powered solutions and automation. The Data Science Manager – Space 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.

Requirements

Must be eighteen years of age or older.
Must be legally permitted to work in the United States.
Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or a related technical field.
7+ years of industry experience in data science, applied machine learning, or software engineering, and 2+ years in a people or technical leadership role.
Strong foundation in applied data science with experience building and maintaining production-grade software, coupled with the ability to set technical direction, establish standards, and drive data science and engineering excellence from concept through production at scale.
Hands-on experience developing AI-powered tools and platforms that integrate seamlessly with existing enterprise software ecosystems.
Proficiency in python and solid understanding of engineering best practices, including APIs, microservices, version control, CI/CD, testing and deployment.
Experience designing and operating large-scale data pipelines and distributed systems with AI/ML components, preferably on cloud platforms such as GCP and VertexAI.
Familiar with microservices architectures and deploying models and APIs in containerized, cloud-neutral environments.
Familiar with or contributed to AI agents or agentic applications in real-world, production settings.
Proven ability to translate complex business processes and strategy into scalable, maintainable architectures, including well-orchestrated data and ML pipelines, while clearly communicating technical constraints and trade-offs to non-technical stakeholders.
The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job.

Nice To Haves

Domain experience in retail, merchandising, e-commerce, or supply chain, or familiarity in space intelligence concepts such as store layout optimization, merchandising analytics, computer vision, and LLM-based solutions.

Responsibilities

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.
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.
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.
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 products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects.
Ensure best practices and vision for data analysis and model productionalization are leveraged.