Sr Data Scientist - AI Engineer
About The Position
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.
Requirements
- Must be eighteen years of age or older.
- Must be legally permitted to work in the United States.
- 5+ years of experience in data science, machine learning engineering, AI engineering, software engineering, or MLOps.
- Bachelor's degree program or equivalent degree in a field of study related to the job.
Nice To Haves
- 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.
Responsibilities
- 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.
- 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.
- 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.
- 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Associate degree
