Lead Data Scientist - Relevancy Sciences (P3764)

Overview

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.

Responsibilities

• Provide technical leadership, ownership, and influence across search and 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.
• Develop innovative search and recommender systems.
• Design and build ML solutions tailored to the unique needs of 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 the use of Generative AI and agent-based approaches.
• Evaluate and improve ML performance.
• 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.
• 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.
• Contribute to the evolution of serving and deployment strategies.
• 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.

Requirements

• 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 – not just exposure to frameworks or techniques.
• 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), with clear judgment on where these techniques apply and where they don't.
• Experience partnering with engineering teams to deploy and maintain machine learning systems in production.
• Understanding of real-time systems, model serving, feature pipelines, and monitoring.
• Ability to make practical tradeoffs between model complexity, performance, latency, and scalability.
• Demonstrated ability to lead technical work across projects and influence direction across data science, engineering, and product teams.
• Experience mentoring or guiding other data scientists and contributing to a strong technical culture.
• Experience working with cloud platforms such as GCP or Azure.
• Experience in retail, e-commerce, or high-scale consumer domains is a plus.

Why Join Our Team?

• Impact millions of people.
• Continuous learning and development.
• Work on new developments in search and recommender systems.

Rewards

• Health: Medical, dental, and vision benefits.
• Wealth: 401(k) with Roth option and matching contribution, Health Savings Account with matching contribution.
• Happiness: Paid time off, paid leave for maternity, paternity, and family care instances.