Position Purpose:

The Data Science organization is advancing merchandising decision-making through production-grade Agentic AI, MLOps, and AIOps capabilities. This Sr. Data Scientist is responsible for establishing foundational operational frameworks (MLOps, LLMOps, AIOps) and building agentic solutions that reason over enterprise data and orchestrate core data science models into production workflows. The role brings together data science, foundational models, retrieval systems, and reusable AI services to create reliable, governed, and scalable solutions that drive business impact.

Embedded within the data science team, the Sr. Data Scientist applies software engineering discipline, operational rigor, and Agentic AI expertise, partnering closely with software engineers and business stakeholders to move solutions from experimentation to production — automating model and agent lifecycles, embedding observability and governance, and orchestrating tools, APIs, and retrieval systems for enterprise decision intelligence.

Key Responsibilities:

• 35% Solution Development – Proficiently design and develop algorithms and models to use against large datasets to create business insights; Execute tasks with high levels of efficiency and quality; Make appropriate selection, utilization and interpretation of advanced analytical methodologies; Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners; Prepare reports, updates and/or presentations related to progress made on a project or solution; Clearly communicate impacts of recommendations to drive alignment and appropriate implementation
• 30% Project Management & Team Support – Work with project teams and business partners to determine project goals; Provide direction on prioritization of work and ensure quality of work; Provide mentoring and coaching to more junior roles to support their technical competencies; Collaborate with managers and team in the distribution of workload and resources; Support recruiting and hiring efforts for the team
• 20% Business Collaboration – Leverage extensive business knowledge into solution approach; Effectively develop trust and collaboration with internal customers and cross-functional teams; Provide general education on advanced analytics to technical and non-technical business partners; Deep understanding of IT needs for the team to be successful in tackling business problems; Actively seek out new business opportunities to leverage data science as a competitive advantage
• 15% 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 products, project documentation, process flowcharts) to ensure solutions are leveraged for future projects; Define best practices and develop clear vision for data analysis and model productionalization; Contribute to library of reusable algorithms for future use, ensuring developed codes are documented

Direct Manager/Direct Reports:

• This position reports to manager or above
• This position has 0 Direct Reports

Travel Requirements:

• Typically requires overnight travel less than 10% of the time.

Physical Requirements:

• Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.

Working Conditions:

• Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.

Minimum Qualifications:

• Must be eighteen years of age or older.
• Must be legally permitted to work in the United States.

Preferred Qualifications:

• 6+ years of experience in data science, machine learning engineering, AI engineering, software engineering, or MLOps, with a focus on production-ready AI solutions.
• 2+ years of hands-on experience developing, deploying, evaluating, or supporting GenAI, LLM-based, or Agentic AI solutions.
• Experience building agentic AI systems and multi-step workflows utilizing tool calling, reasoning and planning, state and memory management, structured outputs, RAG/retrieval systems, embeddings, and API integration.
• Strong software engineering skills, including Python, SQL, automated testing, containerization (Docker/Kubernetes), cloud deployment (GCP preferred), and technical collaboration with Engineering, DevOps, and SRE partners.
• Demonstrated expertise in foundational MLOps/LLMOps/AIOps practices, including CI/CD automation, model/agent registries, versioning, automated testing, monitoring, automated retraining, rollback strategies, release management, and production support.
• Demonstrated expertise in AI observability, operational reliability, and governance practices (e.g., tracing, telemetry, automated alerting, anomaly detection, incident triage, eval harnesses, safety guardrails, model explainability, approval paths, fallback mechanisms, tool-use auditing, cost/latency monitoring, and human-in-the-loop controls).
• Hands-on experience with agent orchestration frameworks, structured agent communication protocols (e.g., MCP, A2A), and Infrastructure-as-Code (IaC).
• Ability to prototype lightweight tools or interfaces, evaluate technical feasibility, and document reusable architectural patterns for future production use.
• Continuous learning agility to evaluate emerging AI architectures, protocols, and operating models.
• Domain experience in merchandising, retail, ecommerce, supply chain, assortment planning, or space planning.
• analysis

Minimum Education:

• 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.

Preferred Education:

• No additional education

Minimum Years of Work Experience:

• 5

Preferred Years of Work Experience:

• No additional years of experience

Minimum Leadership Experience:

• None

Preferred Leadership Experience:

• None

Certifications:

• None

Competencies:

• Attracts Top Talent: Attracting and selecting the best talent to meet current and future business needs
• Business Insight: Applying knowledge of the business and the marketplace to advance the organization's goals
• Collaborates: Building partnerships and working collaboratively with others to meet shared objectives
• Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
• Cultivates Innovation: Creating new and better ways for the organization to be successful
• Customer Focus: Building strong customer relationships and delivering customer-centric solutions
• Develops Talent: Developing people to meet both their career goals and the organization's goals
• Directs Work: Provides direction, delegating and removing obstacles to get work done
• Drives Results: Consistently achieving results, even under tough circumstances
• Nimble Learning: Actively learning through experimentation when tackling new problems, using both successes and failures as learning fodder
• Optimizes Work Processes: Knowing the most efficient and effective processes to get things done, with a focus on continuous improvement
• Self-Development: Actively seeking new ways to grow and be challenged using both formal and informal development channels