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

Our mission: to enable businesses to grow and reimagine themselves by becoming advocates and champions for their Customers. With deep heritage and expertise in retail – one of the world’s most competitive markets, with a deluge of multi-dimensional data – dunnhumby today enables businesses all over the world, across industries, to be Customer First.

Job Title: Senior Applied Data Scientist

Job Location: 3825 Edwards Road, #600, Cincinnati, OH 45209 (May work remotely)

Job Duties:

WHY I DO MY JOB | Job purpose

To lead and execute projects to distil complex problems into compelling insights, using the best of dunnhumby science and making recommendations that resonate with clients and lead them to action. May telecommute 100% of the time from their home office, consistent with dunnhumby’s remote work policy.

WHAT I DO | Key accountabilities

• Build strong relationships with internal contacts & external clients to ensure full understanding of client challenges, growth strategy and agreed measures of success for the project.
• Investigate and implement the most appropriate analytical technique for each project, role modelling the re-use & further development of global solutions or code written by others.
• Deploy data science algorithms and market products on chosen tech stack for efficient and cost-effective delivery.
• Identify opportunities to grow client engagement by proactively solving client’s strategic questions, using a mix of products and advanced data science techniques that support executive decision making and calls to action.
• Participate in client meetings as required to present methodology and solutions through effective story telling techniques.
• Work closely with global products team to provide market feedback for enhancing an existing product offering or augmenting the catalogue with a market-based product that can become global offering.
• Ensure smooth running of your data analytics projects and support junior team members with their projects.
• Lead by example by following dunnhumby Quality Assurance processes, ways of working and coding standards.
• Provide advice and support to colleagues to resolve challenges and support code reviews.

Job requirements:

• Bachelor’s degree (or foreign educational equivalent) in Computer Science, Mathematics, Applied Statistics, Engineering or related field.
• In addition to the degree, must have at least 4 years of experience in each of the following:
• Software development, utilizing methodologies and tools such as Agile (Scrum Master) and Code Merge, and programming in languages including PySpark, Python, Flask/Dash, C++, and HTML; and
• Quality Assurance (QA) and Testing, including the development, execution, and maintenance of automated and manual testing frameworks.

Within this, must have at least 2 years of experience in each of the following:

• Handling large data volumes with modern data processing tools, e.g. by using Hadoop, Spark, SQL, and Python;
• Data Interpretation and Insight Analysis; performing data interpretation, insight generation, and analytical analysis to support business and technical decision-making;
• Programming experience on any standard data mining and modelling packages such as Python and R; and
• Applying advanced statistical models and machine learning algorithms to develop and implement solutions to complex analytical and business problems.

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.

#LI-AB1

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.

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