Senior Data Scientist Consultant, Managed Agent Platform (MAP) – Agent Platform Team P2392 

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 MAP (Managed Agent Platform) squad and is focused on helping stand up a new, centralized platform that simplifies and accelerates the design, development, deployment, and monitoring of AI agents across the organization. 

The MAP is being built in response to growing demand for a flexible, no-code/low-code solution that enables both technical and non-technical users to build and deploy agents without deep engineering overhead. The platform is early-stage — the team is actively evaluating different platforms and tooling, defining workflows, and establishing standards. You will be joining at the ground floor and helping shape what this platform becomes. 

You will be the bridge between the engineers building/maintaining the MAP and the data scientists and agent builders who will use it. You are not building agents for end-clients. Instead, you ensure the platform works for the people on it — that capabilities are tested, documented, and adopted smoothly, and that practitioner needs make it back to the engineering team in a way that shapes the roadmap. 

You’ll bring hands-on familiarity with the open-source agent ecosystem — including frameworks like LangChain, LangGraph, Google ADK, and OpenAI Agents SDK — and apply that knowledge to evaluate platform capabilities, inform design decisions, and help practitioners understand what’s possible. As the platform matures, you’ll help onboard users, develop best practices, and drive adoption across the organization. 

This is a small, engineer-heavy squad that is standing something up from scratch. Everyone pitches in on a bit of everything. Priorities will shift frequently as the platform takes shape. 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 and agent builders and the MAP engineering team. Gather requirements, surface pain points, and translate practitioner needs into actionable feedback that shapes platform decisions, standards, and the roadmap. 

• Platform evaluation & definition: Help evaluate and select platform tooling and contribute to defining core platform capabilities — including agent building workflows, orchestration patterns, tool/API connectivity, observability and tracing, evaluation frameworks, and governance controls. Test and document findings, recommended use cases, limitations, and rollout considerations. 

• Onboarding & enablement: As the platform matures, write guides, maintain documentation, lead training sessions, and run office hours to help data scientists and agent builders adopt the MAP. Serve as a first line of support when teams hit roadblocks. 

• Adoption & communication: Evangelize the MAP to increase awareness and drive adoption across the organization. Produce clear communications and materials for both technical and non-technical audiences. 

• Cross-functional collaboration: Work closely with engineers, the solution architect, and product lead on the squad to ensure platform capabilities have well-defined use cases and that guidelines and best practices are established. Act as an agent-building ambassador to cross-functional teams, explaining recommended workflows and the reasoning behind platform choices. 

• Governance & compliance support: Help validate that platform capabilities meet enterprise requirements around security, compliance, observability, scalability, and data governance. 

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 and SQL. 

• Hands-on experience with one or more agent frameworks (e.g., LangChain, LangGraph, Google ADK, OpenAI Agents SDK, CrewAI, AutoGen) is required. 

• 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, and agentic AI concepts. 

• Exposure to agent platforms is a strong 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 — this platform is early-stage, and priorities will shift frequently as the team learns what works. 

• Strong project and time management skills; able to manage multiple priorities with minimal oversight. 

• Deep curiosity about the agentic AI ecosystem and a genuine desire to become a subject matter expert on agent orchestration, evaluation, observability, and deployment patterns. 

• Comfort working on a small, engineer-heavy team where you are often the only non-engineer voice in the room. 

#LI-SSS

Lead Data Scientist, Relevancy Sciences Data Science & Research – Personalization & Loyalty Strategy (P3764)

Relevancy Sciences Team is responsible for powering relevant, personalized, and scalable customer experiences across Kroger’s e-commerce ecosystem. We build and evolve the science behind search and recommendations that serve millions of customers and support high-scale digital experiences.

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. This role is ideal for someone who combines depth in applied machine learning with strong systems thinking, cross-functional influence, and contributes towards agentic capabilities.

What does the role entail? (Responsibilities)

Technical Leadership, Ownership, and Influence. Own and drive technical initiatives across search & 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 & recommender systems. Design and build ML solutions tailored to the unique needs of 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.

What skills and experience do you need? (Requirements)

• Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 6+ years of experience applying machine learning to real-world problems with strong experience in search and recommender systems. Strong understanding of approaches such as embeddings and multi-stage decision systems.
• Strong proficiency in Python and SQL, with experience working on large-scale data using tools such as Spark.
• Experience with modern machine learning and deep learning frameworks such as PyTorch or TensorFlow.
• Strong foundation in statistics, experimentation, and data analysis, including design of experiments and A/B testing.
• Familiarity with large language models, foundation models, and emerging AI capabilities, including where they are applicable and where they are not.
• Experience evaluating or prototyping GenAI-based solutions is preferred.
• 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? (Rewards)

Impact millions of people. As a member of our data science team, you will have the opportunity to make a tangible difference in the lives of millions of customers by delivering relevant and personalized recommendations that enhance their grocery shopping experience. Your work will directly contribute to increasing customer satisfaction and loyalty, driving business outcomes for our company.

Continuous learning and development. Challenge yourself. We are committed to fostering a culture of continuous learning and development. You will have access to resources and support for expanding your knowledge and skills in cutting-edge technologies, including recommender systems, machine learning and artificial intelligence. Our team encourages exploration and experimentation, providing opportunities to stay at the forefront of industry advancements.

Work on new developments in recommender systems. Join a team at the forefront of innovation in recommender systems and AI. You will have the chance to contribute to pushing the boundaries of what’s possible in personalized recommendation technology. You will have the chance to work on exciting projects that leverage the latest developments in deep learning architectures and data science methodologies.

#LI-SSS

Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States and with the Kroger Family of Companies (i.e. H1-B visa, F-1 visa (OPT), TN visa or any other non-immigrant status)

You will be joining a team of data scientists operating within the PL&S at 84.51°. Our team partners across multiple portfolios, including Our Brands, Kroger Personal Finance, and the Kroger Enterprise Marketing organization, to support media strategy & execution, targeting, measurement, and enterprise level marketing initiatives.

We work closely with the Our Brands and Kroger Personal Finance teams to plan, execute and measure targeted media and deliver insights that drive growth and efficiency. The team leads the execution of brand loyalty mailers for the Big 3 Our Brands, with a focus on rewarding high‑brand‑loyal households that drive the greatest business value. We also collaborate with Kroger Marketing to execute and measure activations across a range of initiatives, ensuring data driven insights inform business decisions.

Our team is known for being subject matter experts of KPM processes for Kroger Model and owners of a broad set of analytical and external-facing tools. We solve complex client and customer problems by applying the most appropriate statistical and analytic approaches to answer meaningful business questions. In this role, you will be on the front lines supporting Sales and Account Management partners, while also developing deep expertise in retailer data.

You will work hands-on with large-scale retailer databases and leverage the full suite of 84.51° technical and visualization tools, including Python, SQL, Excel, Power BI, and internal platforms, to deliver timely, relevant, and actionable insights. We expect all data scientists on our team to be deeply engaged in advanced analytics and comfortable working directly with data to drive impact.

Responsibilities:  

• Lead end-to-end analytics and data science projects, managing stakeholders and delivering solutions on time, within scope, and to business objectives.
• Develop a strong understanding of the end-to-end CRM (Our Brands Loyalty Mailer) targeting and measurement process to ensure the timely, accurate delivery of mailers and the generation of actionable insights. 
• Apply optimizations and scientific enhancements in targeting to improve customer engagement and maximize ROI.
• Partner closely with Sales and Account Management to own the analytic relationship for OB, driving engagement and shaping analytical offerings through feedback and insight.
• Translate complex first-party data into clear, actionable customer stories and recommendations that inform media strategy, optimization, and business decisions.
• Drive continuous improvement through stronger measurement methodology, increased automation, and clearer data visualization that leads to more actionable insights.
• Attain deep expertise in 84.51° and KPM data assets, measurement techniques, QA processes, and analytic capabilities, confidently applying them to internal and external business needs.
• Collaborate across data science, product, engineering, and capabilities teams to validate new use cases, assess data quality, and support innovation.
• Mentor and support junior data scientists through onboarding, coaching, and knowledge sharing, while championing best practices across the data science community.

Qualifications, Skills and Experience: 

• Bachelor’s degree in statistics, mathematics, business analytics, economics, marketing, or related discipline.
• 2+ years of experience in retail, ad technology, digital media, and/or advertising analytics.
• 2+ years of experience in data science, ad technology, digital media, and/or advertising analytics.
• 2+ years of experience in extracting insights from large databases, translating market research into actionable solutions and presenting findings and recommendations to clients.
• 2+ years of experience using advanced algorithms, programming languages (Python, SQL), or technologies in the development of technical analytics solutions, capabilities, and ad hoc analyses.
• Experience working cross-functionally to continuously improve existing solutions based on client feedback and validated demand.
• Strong interpersonal and communication skills including excellent verbal, written, presentation, and consultation skills.
• Comfortable conveying complex data science techniques and methodological approaches to less technical audiences – both internal and external.
• Strong analytical, problem-solving and decision-making skills.
• Strong data visualization and business acumen skills to link analysis to business impact (grocery and/or retail experience is a plus).
• Natural curiosity that welcomes and embraces ambiguity and change, with a willingness to try new things and to fail.
• Excellent attention to detail, organization skills and ability to work in a collaborative environment.

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The Consumer Research team at 84.51° uncovers the “why” that complements the deep behavioral understanding 84.51° uniquely delivers.  Our focus is uncovering the “Why,” integrating it with the “What,” and turning these insights into human stories and business recommendations that compel actions to be taken that delight our customers in ways no other retailer can.  We strive to consistently bring this human understanding to the business and serve as a strategic partner to leaders at 84.51° and Kroger. 

A Senior Researcher on our Research Insights team is accountable for the creation and execution of research projects start to finish, applying best practices and standards to projects, and delivering actionable results to stakeholders- both internal and external. The role will execute research across a range of methodologies (Quantitative and Qualitative).  Additionally, this role is responsible for leveraging existing data for ad hoc analysis to support a holistic view that raises the collective understanding of our customers’ motivations and perceptions.

CORE RESPONSIBILITIES

Research Execution & Delivery:

• Independently execute research projects end‑to‑end once scope and approach are aligned, including:
• Translating business questions into research objectives
• Designing or customizing questionnaires / discussion guides
• Managing vendors and internal partners through fieldwork
• Conducting analysis and synthesis across qualitative, quantitative, and secondary data
• Developing clear, decision‑oriented insights and recommendations
• Apply strong judgment in balancing speed, depth, feasibility, and cost in execution
• Ensure quality, rigor, and clarity across all deliverables

Stakeholder & Project Management:

• Serve as the day‑to‑day research owner for assigned projects
• Proactively manage timelines, dependencies, and risks
• Communicate clearly with partners on status, tradeoffs, and implications
• Show up as a confident research partner in working sessions and readouts

Ways of Working & Team Contribution:

• Operate effectively within CR request intake, scoping, and prioritization processes
• Leverage existing research, products, and tools before defaulting to custom solutions
• Contribute to shared learning by documenting work, sharing best practices, and flagging patterns across projects
• Partner closely with Lead Researchers, Consultants, CX Measurement, and Research Operations

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• Experience executing multiple research projects simultaneously with minimal oversight
• Comfort working across data types (qual, quant, secondary) and synthesizing into clear stories
• Strong analytical thinking and ability to translate findings into implications and recommendations
• Clear, concise communicator—able to present insights to non‑research audiences
• Strong organizational skills and attention to detail
• Comfortable operating in a fast‑moving, sometimes ambiguous environment
• Tools & Platforms (experience with some preferred): Qualtrics, SPSS/R/Python/Alteryx, Tableau/PowerPoint, insights repositories, and qualitative platforms (e.g., VoxPopMe)

EDUCATION:

• Bachelor’s degree or equivalent, ideally in business, marketing, social sciences, psychology or analytics/statistics.
• 3-5 years of industry experience

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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, PySpark and Databricks
• 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.

#LI-SSS

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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SENIOR DATA SCIENTIST (P2408)

SUMMARY:

The Core Science team is responsible for some of our most foundational data science products including enterprise-wide segmentations and store measurement tools. As Senior Data Scientist on the Core Science team, you will build and maintain our new Customer Dimensions and Functional Loyalty. Our team plays a crucial role in building core segmentations that are used across the business in various functions like customer reporting and targeting.

In this role, you will maintain and enhance existing dimensions, support business use of dimensions, build and monitor data quality dashboards, and develop new customer dimensions to expand our view of customers. This includes responding to time-sensitive requests for deep-dive analyses, managing routine processes, and innovation of new segmentations.

You will leverage a diverse technical toolkit including Power BI for dashboard development, GitHub for code management and version control, and PySpark for programming. Additionally, you'll have opportunities to explore the use of embeddings and incorporate AI technologies to enhance our analytical capabilities and create more efficient solutions.

Success in this role requires strong analytical skills and business insights combined with intellectual curiosity and adaptability. We're looking for someone who embraces change, demonstrates enthusiasm for learning new methodologies, and can balance maintaining existing systems while innovating for the future.

RESPONSIBILITIES:

• Effectively managing projects, tasks and stakeholders to ensure that projects are delivered on time, within budget and to brief specification while ensuring minimal downtime and maximum utilization.
• Interpreting results and developing actionable recommendations from the data analysis to build relevant customer stories for clients.
• Identifying and implementing artificial intelligence solutions to enhance business processes, drive operational efficiencies, and deliver increased value to stakeholders.
• Quality Assurance of own code and that of team members to ensure the highest quality work is being delivered to our clients.
• Identifying opportunities for standardization & automation of existing solutions and processes to enable 84.51° to realize the maximum potential from our teams.
• Challenging and improving 84.51° analytical capabilities/products.
• Working closely with key internal stakeholders to ensure sound knowledge of client objectives and customer insight, to subsequently design best-in-class solutions.

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• 2+ years of experience in consulting or professional services
• 2+ years of experience in extracting insights from large databases, translating market research into actionable solutions and presenting findings and recommendations to clients
• 2+ years of experience using advanced algorithms, programming languages, or technologies in the development of technical analytics solutions or capabilities.
• Experience querying data from relational databases using SQL.
• Experience using R, Python, or other similar statistical software to develop analytical solutions.
• Experience with data wrangling, data cleaning and prep, dimensionality reduction.
• Experience with Big Data concepts, tools, and architecture (e.g. Hadoop, Pig, Hive, Spark, Sparkling Water).
• Ability to create computationally efficient solutions.
• Strong academic background in mathematics, statistics, computer science, economics, or similar discipline.
• Data visualization skills and ability to present technical solutions to non-technical audience.
• Strong interpersonal and communication skills.
• Strong analytical, creative problem-solving and decision making skills
• Strong business acumen; grocery and/or retail experience is a plus.
• Passionate about data, analysis and insights.
• Natural curiosity that welcomes and embraces change and willingness to try new things and to fail, particularly in exploring emerging technologies and AI applications.
• Ability to work in a highly collaborative environment.

#LI-SSS

DIRECTOR, DATA SCIENCE - SUPPLY CHAIN, OPERATIONS, REPLENISHMENT

Cincinnati, OH

Chicago, IL

SUMMARY:

The Supply Chain, Operations, and Replenishment  (SCORe) team is responsible for creating for cutting edge supply chain and operations sciences to meet Kroger’s need now and in the future, ensuring that we deliver value for our Kroger stakeholders and the enterprise.  This role will lead the supply chain data scientists and partner heavily with our Product and Engineering community to develop the best solutions for our stakeholders within our supply chain workstreams.

RESPONSIBILITIES:

• Influence and partner with SCORe team leaders to set the vision and future road map by developing immediate and long-term strategic plans in supply chain and operations
• Partner with the other SCORe Tech DSR Leads to develop and oversee innovation/capability roadmaps to support strategic supply chainplans
• Bring transformational, innovative thinking to the SCORe team, creating and/or improving ways of working, products, sciences, tools and initiatives
• Develop and manage stakeholder relationships within 84.51° and Kroger to drive better decisions across the Supply Chain space
• Understand business priorities and project value to make prioritization decisions
• Partner with cross-functional leaders to ensure shared goals that enable delivery of science services and insights at scale
• Enable & manage a team of data scientists in delivering strategic objectives and work plans while developing their individual technical and business skills

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• 7+ years developing analytical solutions using advanced AI, Machine learning, and optimization methods
• 7+ years querying data from relational databases using SQL/Python to develop analytical solutions
• 7+ years with data wrangling, data cleaning and prep, problem framing, and analysis
• Experience with managing, recruiting, training, developing, coaching, and/or inspiring highly technical associates
• Proficient in technology stack (Azure, Databricks, Python, Spark)

• Experience applying data science capabilities to supply chain and operations business problems

• Experience with developing, coaching, and/or inspiring highly technical associates
• Ability to work in a highly collaborative cross functional environment
• Comfort with fast pace and multiple moving parts, anticipating and solving for future barriers/ changes 
• Excellent interpersonal, collaboration, and communication skills
• Strong business acumen
• Strong academic background in mathematics, statistics, computer science, economics, or similar discipline
• Strong analytical, creative problem-solving and decision-making skills
• Ability to create computationally efficient solutions
• Natural curiosity and passion around data, analysis and insights
• Welcomes and embraces change
• Ability to work fast yet accurately
• An openness and willingness to try new things and to fail 

An ideal candidate has a strong track record of: 

• Building stakeholder relationships at a senior level
• Internal or External client exposure and strong understanding of client’s business needs
• Ability to present technical solutions to non-technical audience
• Managing, recruiting, training, developing, coaching, inspiring and/or leading others
• Championing an inclusive culture and embracing 84.51° D&I initiatives 
• Creating and/or improving large-scale processes or initiatives
• Leading Data Scientists through product life cycle and projects
• Independently creating solutions and solving open-ended and/or complex problems, analyzing both successes and failures
• Collaborating across all functions of 84.51° 
• Providing direct, constructive feedback to peers and junior data scientists 
• Making data-driven decisions and priority calls
• Clearly articulating their point-of-view
• Comfortability with fast pace and multiple moving parts, anticipating and solving for future barriers/ changes 

#LI-SSS

Sr. ML Data Engineer, Relevancy Sciences – Personalization & Loyalty Strategy (P508)

The Relevancy Sciences Team is responsible for creating relevant and personalized customer experiences for Kroger's E-commerce platform, which ranks among the top 10 ecommerce companies in the US. We generate trillions of recommendations at scale and deliver them to millions of Kroger customers daily. Our team maintains a comprehensive portfolio of machine learning solutions for search & product recommendations. We are seeking a talented and experienced Senior ML Data Engineer to join our data science team, with specialized expertise in building search and recommender systems.

Role Overview

You will architect, build, and operate the critical data infrastructure that powers our machine learning models, spanning from feature engineering to training data generation. This role serves as the bridge between ML requirements and production data systems, with ownership of feature stores, training/evaluation pipelines, and ML-specific data operations. You will enable data scientists to iterate rapidly while ensuring production-grade reliability and scalability.

What You'll Do

1. Feature Store Operations & Governance (40%)

• Own the feature request lifecycle from intake through deployment, driving reusability and maintaining a searchable feature catalog
• Design and build scalable feature pipelines that compute features from diverse sources (BigQuery, Azure Data Lake) and write to Feature Store infrastructure (Vertex AI Feature Store + BigQuery)
• Build streaming feature engineering pipelines using Apache Beam/Dataflow for real time feature computation and low-latency model serving with sub-second data freshness
• Ensure point-in-time correctness and online/offline feature consistency to prevent data leakage
• Implement drift detection, data quality monitoring, and alerting mechanisms
• Develop self-service tools and templates that enable teams to independently create features

1. Training & Evaluation Data Pipelines (30%)

• Build automated pipelines that generate ML-ready training datasets by combining features with labeled target variables
• Implement point-in-time correctness logic and sophisticated sampling strategies to ensure balanced, representative datasets
• Maintain comprehensive dataset versioning for full traceability across model versions
• Generate detailed evaluation reports with performance metrics segmented by business dimensions
• Support operations across both Azure and Vertex AI environments during platform migration

1. ML Data Operations & Reliability (20%)

• Serve as Tier 2/3 on-call responder for feature data quality incidents, diagnosing and resolving pipeline failures and performance issues
• Maintain comprehensive lineage tracking and metadata management for full data traceability
• Support regulatory compliance through proper data governance and documentation

1. Standards, Education & Collaboration (10%)

• Establish and enforce feature naming conventions, data quality thresholds, and point-in-time correctness patterns
• Conduct workshops on feature engineering best practices and provide expert guidance on feature design
• Partner with Data Scientists, ML Engineers, Data Engineering, and MLOps teams to optimize infrastructure and align with technical strategy

What We're Looking For

Required Qualifications:

• 3+ years of hands-on experience building and maintaining ML data pipelines in production environments with demonstrated expertise in scaling and reliability
• Expert-level SQL skills and advanced Python programming capabilities with experience in data processing frameworks and ML libraries
• Proven experience with cloud data platforms, with strong preference for GCP ecosystem including BigQuery, Dataflow, Vertex AI Feature Store, and associated ML services
• Deep understanding of end-to-end ML workflows including training data preparation, model evaluation methodologies, and serving infrastructure requirements
• Production operations mindset with experience in monitoring, alerting, on-call responsibilities, and meeting SLA commitments

Strongly Preferred Qualifications:

• Hands-on experience with Feature Store platforms such as Vertex AI Feature Store, Feast, Tecton, or similar enterprise solutions
• Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices
• Multi-cloud experience with both Azure and GCP platforms, including data migration and hybrid cloud architectures
• Strong familiarity with core ML concepts including feature engineering, label creation, train/test/validation splits, and data leakage prevention
• Background spanning both analytics engineering and ML-specific data engineering with understanding of the unique requirements of each domain

Success Indicators:

• Improved Data Science Productivity:Data Scientists spend significantly less time on data preparation and infrastructure concerns, enabling more focus on model development and experimentation
• Increased Feature Reuse:Measurable increase in feature reuse across multiple models and teams, reducing redundant development effort and improving consistency
• Reliable Automation:Training and evaluation data generation processes operate reliably with minimal manual intervention and high uptime
• Efficient Incident Response:Data quality incidents are triaged quickly with clear escalation paths and rapid resolution times
• Accelerated ML Iteration:Overall ML model development and iteration velocity improves measurably across all teams using the platform

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