Lead Data Scientist; P3764

84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.

Powered by cutting-edge science, we utilize first‑party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer‑centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.

84.51° follows a 5‑day in‑office work schedule to support collaboration, alignment, and team connection.

Relevancy Sciences Team is responsible for powering relevant, personalized, and scalable customer experiences across Kroger’s e‑commerce ecosystem. We build and evolve the science behind search and recommendations that serve millions of customers and support high‑scale digital experiences.

We are seeking a Lead Data Scientist to provide technical leadership across search and recommender systems, with a strong focus on modern model architectures, and production‑ready machine learning. This role is ideal for someone who combines depth in applied machine learning with strong systems thinking, cross‑functional influence, and contributes towards agentic capabilities.

• Own and drive technical initiatives across search & recommender systems. Define and evolve the science strategy for improving content discovery, relevance, personalization, and decision support across digital experiences. Identify high‑impact opportunities, make clear technical tradeoffs, and guide the team towards scalable, practical solutions.
• Rapidly prototype and validate new ideas to accelerate adoption and demonstrate measurable value.
• Design and build ML solutions tailored to the unique needs of grocery retail domain. Lead the development of systems that improve product discovery and personalization across customer journeys. Bring strong technical and thought leadership on next generation personalization, including the use of Generative AI and agent‑based approaches.
• Establish rigorous evaluation methodologies to assess the performance of ML systems across key metrics. Define robust online evaluation frameworks, and guide experimentation strategies that connect model improvements to customer and business outcomes.
• Partner closely with Engineering to build and deploy production‑ready ML systems. Influence design decisions related to real‑time inference, feature access, system integration, monitoring, and reliability. Ensure solutions meet latency, scalability, and operational requirements.
• Work closely with Product, Engineering, and business stakeholders to translate needs into clear problem statements, hypotheses, and execution plans. Drive alignment across teams and influence decisions through clear communication of tradeoffs, risks, and expected outcomes.
• Mentor data scientists and lead technical reviews to improve model quality, experimentation rigor, and systems thinking. Promote best practices in reproducibility and evaluation. Contribute to building a strong, learning‑oriented team culture.

• Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 6+ years applied ML with explicit search and/or recommendation systems experience.
• Demonstrated experience designing and building systems at scale — including representation learning, candidate retrieval and ranking with multi‑stage pipelines.
• Proficient in Python and SQL, with experience processing large‑scale data in distributed environments (e.g., Spark).
• Track record of shipping ML systems that moved business or customer metrics at scale.
• Strong foundation in statistics, experimentation, and data analysis, including design of experiments and A/B testing.
• Hands‑on experience building or rigorously evaluating LLM‑ and agent‑based systems (e.g., RAG, agentic workflows, LLM‑based evaluation).
• Experience partnering with engineering teams to deploy and maintain machine learning systems in…