Lead Data Scientist; P3764

84.51° Overview

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. Join us °.

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.

• Technical Leadership, Ownership, and Influence – Own and drive technical initiatives across search & recommender systems. Define and evolve science strategy to improve content discovery, relevance, personalization, and decision support. Identify high‑impact opportunities, make clear technical tradeoffs, and guide the team toward scalable, practical solutions. Rapidly prototype and validate new ideas to accelerate adoption and demonstrate measurable value.
• Develop innovative search & recommender systems – Design and build ML solutions tailored to the 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 Generative AI and agent‑based approaches.
• Evaluate and improve ML performance – Establish rigorous evaluation methodologies to assess performance across key metrics. Define robust online evaluation frameworks and guide experimentation strategies that connect model improvements to customer and business outcomes.
• Model serving and deployment – 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.
• Cross‑functional leadership – 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.
• Mentoring and knowledge sharing – 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 of experience applying machine learning to real‑world problems with strong experience in search and recommender systems. Strong understanding of embeddings and multi‑stage decision systems.
• Strong proficiency in Python and SQL, with experience working on large‑scale data using tools such as Spark.
• Experience with modern machine learning and deep learning frameworks such as PyTorch or Tensor Flow.
• Strong foundation in statistics, experimentation, and data analysis, including design of experiments and A/B testing.
• Familiarity with large language models, foundation models, and emerging AI capabilities.
• Experience evaluating or prototyping GenAI‑based solutions is preferred.
• Experience…