Position Purpose:

We are transforming how merchandising decisions are made through AI-powered solutions and automation. This Lead Data Scientist plays a key role in designing and building state-of-the-art GenAI and Agentic AI Systems that enables autonomous decision recommendations. The position focuses on orchestrating LLMs with existing foundational models to deliver adaptive, learning decision systems that increase efficiency and improve customer experience.

This role leads the end-to-end implementation of agentic AI initiatives, from architecture design and experimentation through evaluation and ongoing governance in production.  The Lead Data Scientist partners closely with data science teams, product managers, software engineers, and business stakeholders to embed foundational science models into agentic workflows, design AI orchestration platforms, and promote scalable services frameworks in production. The role establishes best practices and internal standards for Agentic AI development, testing, and monitoring, communicates technical and business insights to diverse audiences, and provides strategic technical leadership across cross-functional teams.

Key Responsibilities:

30% Solution Development - Utilize expertise when designing and developing algorithms and models to use against large datasets to create business insights; Make appropriate selection, utilization and interpretation of advanced analytics methodologies; Effectively communicate insights and recommendations to both technical and non-technical leaders and business customers/partners; Clearly communicate impacts of recommendations to drive alignment and appropriate implementation25% Project Management & Team Support - Lead and manage large and complex projects and teams; 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; Serve as a technical subject matter expert (SME) for one or more data science methods, both predictive and prescriptive; Lead data science communities across several business units20% Business Collaboration - Leverage extensive business knowledge into solution approach; Effectively develop trust and collaboration with internal customers and cross-functional teams; Provide technical education on advanced analytics to data science community; Partner with IT to understand potential for new tools and ways to maintain technical agility for data science; Actively seek out new business opportunities to leverage data science as a competitive advantage25% Technical Exploration & Development - Seek further knowledge on key developments within data science by attending conferences and publishing papers; 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; Ownership of library of reusable algorithms for future use, ensure developed code/models are documented; Develop mastery in one or more prescriptive modeling techniques, like optimization, computer vision, recommendation, search or NLP

Direct Manager/Direct Reports:

This position typically reports to manager or aboveThis position has 0 Direct Reports and leads/manages projects

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.Demonstrated expertise in predictive modeling, data mining and data analysisDemonstrated expertise utilizing statistical techniques to identify key insights that help solve business problems

Preferred Qualifications:

10+ years of combined AI engineering experience or equivalent in software engineering or ML engineering.2+ years of experience developing/deploying GenAI or LLM-driven automated systems, and Agentic AI evaluation frameworks at scale.Hands-on experience with LLM agent orchestration frameworks (LangGraph, LangChain etc.).Strong understanding of structured agent interactions (eg., MCP and A2A)Proven experience deploying models and APIs in containerized, cloud-neutral environments.Proficiency in prompt engineering, RAG implementation, and LLM fine tuning.Familiarity with agentic IDEs (eg., GitHub Copilot, Cursor, etc.)Experience with GCP cloud computing platform.Practical knowledge of vector databases and exposure to knowledge graphs (e.g., Neo4j), embeddings, or multimodal data ingestion.Comfortable collaborating with front-end developers or building light UI prototypes (e.g., Streamlit, React).Contributions to open-source AI/ML tooling or orchestration frameworks.Domain experience in merchandising, retail, ecommerce, or supply chain.

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:

10

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 needsBuilds Networks: Effectively building formal and informal relationship networks inside and outside the organizationBusiness Insight: Applying knowledge of the business and the marketplace to advance the organization's goalsCollaborates: Building partnerships and working collaboratively with others to meet shared objectivesCommunicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiencesCultivates Innovation: Creating new and better ways for the organization to be successfulDevelops Talent: Developing people to meet both their career goals and the organization's goalsInstills Trust: Gaining the confidence and trust of others through honesty, integrity, and authenticityOptimizes Work Processes: Knowing the most efficient and effective processes to get things done, with a focus on continuous improvementPersuades: Using compelling arguments to gain the support and commitment of othersSelf-Development: Actively seeking new ways to grow and be challenged using both formal and informal development channelsStrategic Mindset: Seeing ahead to future possibilities and translating them into breakthrough strategiesTech Savvy: Anticipating and adopting innovations in business building digital and technology applications