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- +3
- +7
- United States
À propos
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. Join us at 84.51°! Responsibilities
Partner with senior data scientists and engineers to develop and test audience creation and recommendation solutions, including natural language–driven workflows. Query, clean, and transform large-scale customer datasets (loyalty, clickstream, digital interaction data) to support audience modeling and campaign targeting. Apply foundational statistics and machine learning techniques to measure customer behavior and campaign performance. Build and share insights and visualizations that translate technical findings into clear customer and business stories. Follow best practices for coding, quality assurance, version control, and documentation to ensure work can be scaled and reused. Actively participate in team discussions, retrospectives, and knowledge-sharing sessions to accelerate learning and contribute to team success. Package building and code optimization experience or a strong desire to learn. Collaborate closely with teammates across product, engineering, and science to learn how solutions are scaled and operationalized. Challenge and improve 84.51° analytical capabilities and products. Qualifications, Skills, And Experience
Bachelor’s degree in a quantitative field (Statistics, Data Science, Computer Science or related discipline). Experience querying data from relational databases using SQL. Experience (academic projects, internships, or research) using R, Python, or other similar statistical software to develop analytical solutions. Exposure to data wrangling, cleaning, and dimensionality reduction techniques. Foundational understanding of machine learning concepts (classification, regression, clustering). Experience with Big Data concepts, tools, and architecture (e.g. Spark, Databricks, PyTorch). Strong communication skills, with the ability to explain technical ideas to non-technical audiences. Curiosity, adaptability, and a strong desire to learn from senior data scientists and cross-functional partners. Ability to work in a highly collaborative environment. DESIRED
Grocery and/or retail experience is a plus. NLP and Large Language Models (LLMs): Exposure to prompt engineering, intent extraction, or modern LLM APIs (OpenAI, Hugging Face). Schema and Taxonomy Design: Interest in defining structured data schemas and normalizing free-text for downstream modeling. Semantic Search & Embeddings: Familiarity with vector databases and embedding models for product/theme matching and retrieval. Evaluation Frameworks: Awareness of metrics like precision/recall, F1, and regression testing for model quality. Optimization & Recommender Systems: Understanding of how predictive propensities feed into recommendation workflows. Data-to-Insight UX Integration: Exposure to working with product/engineering teams on APIs or UI flows that surface AI-driven recommendations. Feedback Loop Design: Interest in methods for capturing user interactions and feeding them back into model improvement pipelines. Pay Transparency And Benefits
The stated salary range represents the entire span applicable across all geographic markets from lowest to highest. Actual salary offers will be determined by multiple factors including geographic location, relevant experience, knowledge, skills, and alignment with market data and cost of labor. In addition to salary, this position is also eligible for variable compensation. Benefits include: Health: Medical, dental, and vision coverage with in-network and out-of-network options. Wealth: 401(k) with Roth option and matching, Health Savings Account with matching contribution (requires enrollment in qualifying medical plan), AD&D and supplemental insurance options. Happiness: Hybrid work environment, paid time off with generous vacation and paid leave for family events. Seniority level
Entry level Employment type
Full-time Job function
Engineering and Information Technology Industries
Business Consulting and Services
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Compétences idéales
- Big Data
- Machine Learning
- PyTorch
- Python
- R
- SQL
- Spark
Expérience professionnelle
- Machine Learning
- NLP
- Data Scientist
Compétences linguistiques
- English
Avis pour les utilisateurs
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