Senior Data Scientist (P3358)

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 at 84.51°!

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Senior Data Scientist / P3358

Summary

We are seeking a Senior Data Scientist to join our Proprietary Models team. In this role, you will play a critical part in developing, evaluating, and democratizing our foundation models portfolio—including embeddings, pre-trained LLMs, and fine-tuned language models. You will build and optimize embedding pipelines, design rigorous evaluation frameworks, drive models from experimentation to production, and guide business stakeholders through the process of integrating these models into their workflows. The ideal candidate combines deep technical expertise in machine learning and AI with strong problem-solving abilities, a commitment to engineering excellence, and a passion for enabling others to leverage AI effectively.

Responsibilities

Your work will cover a lot of ground, from engineering data pipelines to testing novel AI methodologies to guiding business stakeholders. You will be expected to flex between these skill domains as work progresses through its lifecycle, and to collaborate within a team to make it all happen.

• Develop and optimize embedding pipelines for vector search, retrieval-augmented generation (RAG), and downstream ML applications.


• Conduct rigorous model testing, evaluation, and validation against quality metrics, performance benchmarks, and reliability standards prior to release.


• Design and execute proof of concepts to demonstrate feasibility for specific business problems.


• Build and maintain automated testing frameworks for model performance, data quality, and pipeline integrity.


• Collaborate with MLE/Researchers/Engineers to develop, fine-tune, and deploy foundation models.


• Perform exploratory data analysis (EDA), feature engineering, and data preprocessing to support model development.


• Develop and maintain Jupyter notebooks, scripts, and workflows for reproducible experimentation and analysis.


• Contribute to building and maintenance of code packages, APIs, hosted applications, and other foundation model delivery mechanisms.


• Troubleshoot integration challenges and provide technical guidance for model integrations.


• Guide business stakeholders, data science and engineering teams through adoption and integration of foundation embedding and generative AI models.


• Create documentation, user guides, and best practices for internal model adoption and usage.


• Serve as a liaison between the Foundation Models team and business stakeholders, gathering feedback to inform the development roadmap.

Qualifications and Experience

Required

• Bachelor’s degree in Data Science, ML, Computer Science, or a related discipline


• 3+ years of hands-on experience in data science or machine learning roles


• 3+ years’ experience building in Python using big data tools (e.g. Hadoop, Databricks)


• Hands-on experience building processes incorporating LLMs, embeddings, or related model architecture


• Experience applying embeddings within AI projects and machine learning applications


• Track record of shipping, measuring, and maintaining machine learning models in production


• Exceptional communication skills with ability to explain complex technical concepts to non-technical audiences


• Experience in stakeholder management, technical documentation creation, and cross-functional collaboration

Preferred

• Experience building in cloud data platforms (i.e. Azure, GCP)


• Previous developer relations, technical evangelism, or advocacy experience


• Background in NLP, conversational AI, or generative AI applications


• Open-source contributions or thought leadership in AI/ML community

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