Data Scientist

Responsibilities:

• Transforming how we operate from Source to Table by reimagining planning, ordering, inventory, and execution as a deeply connected, AI-enabled ecosystem. In this role, you will own the technical roadmap for an AI-native data science organization that builds the predictive, prescriptive, and agentic intelligence powering our next-generation supply chain and store execution platforms.
• You will define and implement the overarching data science and AI architecture and technical strategy for a team of data scientists that will build data science and AI capabilities to optimize operations, processes, and capabilities in the Source to Table transformation area that would include 1) end-to-end demand forecasting; 2) inventory optimization to minimize out-of-stocks; 3) store and warehouse replenishment to minimize supply chain waste; 4) Agentic AI frameworks to augment and automate planning and execution capabilities.
• You will be hands-on with the design, implementation, and deployment of data science and AI models to convert multiple point solutions to connected data science and AI solutions at scale.
• Stay current with the latest advancements in data science, machine learning, and AI technologies. Evaluate and implement new tools and technologies to enhance the data science architecture and continuously improve analytical capabilities. Drive innovation through research, experimentation, and prototyping.
• Build and scale AI-native components: models + agents + reasoning layers. Build a portfolio that includes:
• Lead partnerships with cross-functional teams, including product, engineering, and domain experts, to deliver analytical, predictive, and decision-support solutions for supply chain and fulfillment-related functions and capabilities. Interface with cross-functional leaders and represent points of view from data science to drive measurable success
• Recruit, build, and lead high-performing teams of data scientists in a technical capacity. Inspire and deliver data science innovations that fuel the growth of the business
• Provide technical and thought leadership in data science and AI. Guide architectures, data science models, machine learning algorithms, and engineering best practices. Provide technical mentorship and guidance to the data science team.
• Use machine learning and AI to solve real customer problems and power the next generation of experiences for large-scale applications in real time. Deliver breakthrough benefits to customers using personalized, enriched, and derived data from a range of sources
• Drive meetings and lead discussions. Prioritize projects across the team and allocate resources to meet business and team goals
• Communicate sophisticated machine learning and modeling solutions and capabilities effectively with intuitive visualizations for business stakeholders
• Core ML/optimization models (forecasting, bias correction, replenishment, shrink/markdown, execution signals).
• Agentic AI frameworks that augment and automate planning and execution, with human-in-the-loop where needed and autonomous where safe/valuable.
• LLM-powered explanation and decision support that translates outputs into natural language.

Requirements:

• An advanced degree in a STEM field like CS, DS, engineering, statistics, math, etc. is preferred, or equivalent experience. PhD strongly preferred in computer science, machine learning, statistics, operations research, applied mathematics, industrial engineering, or a closely related quantitative field (or equivalent depth of research + applied production experience).
• 3+ years of industry experience in applying data science: experimental design, machine learning, deep learning algorithms, operations research, applied mathematical optimization, etc., at scale
• Specific experience and skills working in the retail and grocery industry are preferred, including in the areas of supply chain optimization, merchandising and pricing, digital/ecommerce, fulfillment, and customer and marketing analytics and intelligence capabilities using data science, AI, and machine learning tools and techniques
• You will bring product thinking and deep expertise in systems design to this role.
• You will bring an understanding of how planning and operations connect with customer-related work from a data science perspective.
• You will have prior experience as a consultant and bring a service mindset to the work of business transformation.
• You will be equally comfortable interacting with scientists, engineers, product, and business clients.
• Business acumen and retail understanding are critical.
• Getting stuff done is critical.
• Has to be able to clearly set a vision for partners for the art of the possible.
• Track record of guiding teams through unstructured technical problems to deliver business impact.
• Ability to translate high-level business objectives into action
• Skilled at running cross-functional relationships and communicating with leadership across multiple organizations.
• 3+ years of experience and proficiency in Python and SQL.
• 3+ years of hands-on experience in building data science solutions and production-ready systems on big data platforms such as Snowflake, Spark, and Hadoop
• 3+ years of experience with data engineering, MLOps, and model life cycle management.
• Excellent communication skills, with the ability to synthesize, simplify, and explain complex problems to different audiences.
• Experience with logistics and supply chain management systems, Snowflake, and Azure Databricks is a plus.
• Proven track record of leading data science projects and teams to architect and deploy data science solutions at scale.
• Skills Required: Machine learning, deep learning, applied optimization, operations research, Python, SQL, AI/machine learning tools (Databricks, Vertex AI, etc. ), systems design, analytics across functions, Data Science team leadership (5+ years).
• Strong cross-functional relationship management and executive communication skills.
• Skills Preferred: Big data platforms (Snowflake, Spark, Hadoop), data engineering, MLOps, model life cycle management, experimental design, and strategic retail and grocery analytics expertise.