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 Consultant, SPI – Agent Platform Team

Summary

The Agent Platform Team, within AI Enablement at 84.51°, owns and manages the core platforms that power data science and AI across the organization. This role sits on the SPI (Science Platform Innovation) squad and is focused specifically on the Databricks environment and its evolving AI/ML capabilities.

You will be the bridge between the engineers building and maintaining our Databricks platform and the data scientists who use it every day. You are not building models for end-clients. Instead, you ensure the platform behaves as expected for the people on it—that new capabilities are tested, documented, and adopted smoothly, and that practitioner pain points make it back to the platform team in a way that shapes the roadmap.

You’ll build deep familiarity with Databricks’ infrastructure and AI powered capabilities —including Genie Code, Genie spaces, Agent Bricks, MLflow, and other features as they emerge—as well as the Unity Catalog governance layer that underpins them. You’ll test and evaluate new Databricks features and relevant third-party integrations from the perspective of an end-user, document your findings, and help the team decide what to adopt, how to roll it out, and what to skip.

This is a small, highly collaborative squad. Everyone pitches in on a bit of everything. Priorities shift frequently as the Databricks roadmap evolves. Success in this role is less about fixed technical expertise and more about comfort with ambiguity, good judgment about where to spend your time, and the ability to influence without direct authority.

Responsibilities

• Voice of the practitioner: Serve as the primary liaison between data scientists using Databricks and the platform engineering team. Gather requirements, surface pain points, and translate practitioner needs into actionable feedback that shapes platform decisions and roadmaps.
• Technology evaluation: Lead the testing and evaluation of new Databricks capabilities (e.g., Mosaic AI features, Model Serving enhancements, Genie, new Unity Catalog functionality) and relevant third-party tools. Document findings, recommended use cases, limitations, and rollout considerations. Over time, this may evolve from hands-on beta testing toward designing and coordinating evaluation efforts across groups of practitioners.
• Onboarding & enablement: Write guides, maintain documentation, lead training sessions, and run office hours to help data scientists adopt new platform capabilities. Partner with Learning & Development where appropriate. Serve as a first line of support when teams hit roadblocks.
• Adoption & communication: Evangelize Databricks capabilities to increase awareness and drive adoption. Produce clear release notes, communications, and materials for both technical and non-technical audiences.
• Cross-functional collaboration: Work closely with the solution architect, cloud engineer, and product senior on the squad to ensure proposed technologies have well-defined use cases and that guidelines and best practices are established. Act as a data science ambassador to cross-functional teams, explaining recommended workflows and the reasoning behind platform choices.
• Governance & compliance support: Help validate that new platform capabilities meet enterprise requirements around security, compliance, observability, and data governance (e.g., Unity Catalog lineage, Azure connectivity).

Qualifications & Experience

• Bachelor’s degree in mathematics, statistics, computer science, economics, or a related discipline.
• 2–4 years of experience in data science, AI/ML development, or applied analytics.
• 2–4 years of experience in tech consulting, retail analytics, or related professional services.
• Hands-on experience with Python, SQL, and PySpark.
• Experience gathering user needs, documenting requirements, and driving issues to resolution with technical teams.
• Experience partnering with software engineering and/or product management teams.
• Familiarity with LLMs, prompt engineering, or agentic AI concepts is a strong plus.
• Experience with the Databricks ecosystem (Unity Catalog, MLflow) is a strong plus.
• Exposure to agent frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen) is a plus.

Skills

• Strong written and verbal communication, including the ability to explain technical concepts clearly to non-technical audiences and produce high-quality documentation.
• Ability to influence across teams and seniority levels without direct authority.
• Comfort operating in ambiguity—priorities shift frequently, and you need good instincts about where to focus.
• Strong project and time management skills; able to manage multiple priorities with minimal oversight.
• Curiosity and enthusiasm for emerging AI technologies and a genuine desire to become a subject matter expert on the Databricks platform and its AI capabilities.

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