# Lead Data Scientist - Relevancy Sciences (P3764) **Company**: Relevancy Sciences **Location**: Cincinnati, OH; Chicago, IL **Work arrangement**: hybrid **Experience**: senior **Job type**: full-time **Salary**: $125,000-$207,000 USD **Category**: Data Science **Industry**: Technology **Apply**: https://job-boards.greenhouse.io/8451/jobs/8618262002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_ab1846e5-5b2 ## Description ### 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. ## Skills ### Required - Python - SQL - Spark - Machine Learning - Statistics - Experimentation - Data Analysis - LLM - Agent-based systems - Cloud platforms ### Nice to have - Retail - E-commerce - High-scale consumer domains --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/8451/jobs/8618262002?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)