Senior Data Scientist - Recommender Systems (P508)

Join the Leading Edge of Retail Data Science at 84.51°

84.51° is a premier retail data science, insights, and media company. We partner with The Kroger Co., consumer packaged goods companies, agencies, publishers, and affiliates to create personalized, valuable experiences for shoppers throughout their purchasing journey.

Leveraging state-of-the-art technology, we harness first-party retail data from over 62 million U.S. households through the Kroger Plus loyalty card program. This data powers a customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing, and our retail media advertising solution, Kroger Precision Marketing.

We invite you to be a part of our dynamic team at 84.51°!

Senior Data Scientist, Relevancy Team – Personalization & Loyalty Strategy (P508)

About the Relevancy Team

Our Relevancy Team aims to create relevant and personalized experiences for Kroger's E-commerce site, one of the top 10 e-commerce companies in the US. We deliver trillions of recommendations to millions of Kroger customers. Our science portfolio includes product and coupon recommender systems, substitute recommendations, and shoppable recipes.

Role Responsibilities

• Develop Innovative Recommender Systems: Design, develop, and implement custom recommender systems tailored to the unique needs of the grocery retail industry. Utilize advanced machine learning, including deep learning models, for personalized recommendations.
• Evaluate and Improve Recommendation Performance: Establish rigorous methodologies to evaluate recommendation algorithms. Conduct A/B testing and offline evaluations, perform root cause analysis, and model interpretability studies to identify improvement opportunities.
• Enhance Personalization and Diversity: Enhance personalization to reflect individual preferences, dietary restrictions, and shopping habits. Diversify recommendations to introduce users to a broader range of products.
• Model Serving and Deployment: Collaborate with ML engineers for efficient deployment of recommender system models. Utilize technologies like Docker for robust model serving and scalability in a production environment.
• Collaborate with Cross-Functional Teams: Work closely with data scientists, data engineers, and full stack engineers. Collaborate with product management and business leads to understand objectives and drive optimization efforts.
• Analytics and Insights Generation: Integrate diverse data sources to build datasets for model development. Develop analytics pipelines and reporting dashboards to track performance metrics and the effectiveness of recommendation strategies.
• Document and Knowledge Sharing: Document best practices and technical insights. Contribute to internal tools, libraries, and documentation. Participate in knowledge-sharing sessions and tech talks to foster a culture of continuous learning.

Required Skills and Experience

• Bachelor’s or Master’s degree in computer science, data science, statistics, mathematics, analytics, or a related discipline.
• 2+ years of experience building deep learning models for large-scale recommender systems.
• Proficiency in ML frameworks such as TensorFlow or PyTorch.
• Skilled in SQL, Python, and Spark for data analysis and manipulation. Experience with Databricks is a plus.
• Proficiency with statistics, design of experiments, and exploratory data analysis.
• Experience with cloud platforms like Azure or GCP.
• Experience in Data Engineering and MLOps is desirable.
• Strong independence in developing and owning toolkits, pipelines, and dashboards.
• Excellent problem-solving and analytical skills with attention to detail.
• Prior experience in the retail or e-commerce industry is a plus.
• Ability to communicate complex ideas effectively to both technical and non-technical stakeholders.

Why Join Our Team?

• Impact Millions: Contribute to enhancing the grocery shopping experience for millions of customers by delivering personalized recommendations. Your work will directly influence customer satisfaction and loyalty.
• Continuous Learning and Development: Take advantage of resources and support to expand your knowledge and skills in cutting-edge technologies, including recommender systems and machine learning.
• Innovate in Recommender Systems: Join a team at the forefront of innovation in personalized recommendation technology. Work on exciting projects that leverage the latest advancements in deep learning and