Lead Data Scientist-Deep Learning Specialist

What You Will Be Doing

Albertsons Companies is transforming how we operate from Source to Table—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 predictive, prescriptive, and agentic intelligence powering our next‑generation supply chain and store execution platforms. The position will be based in Pleasanton, CA.

• Define and implement the overarching data science and AI architecture and technical strategy for a team of data scientists to optimize operations, processes, and capabilities in the Source to Table transformation area.
• Drive end‑to‑end demand forecasting, inventory optimization to minimize out of stocks, and store and warehouse replenishment to minimize supply chain waste.
• Design, implement, and deploy scientific and AI models to convert multiple point solutions into connected data science and AI solutions at scale.
• Evaluate and implement new tools and technologies to enhance the data science architecture and continuously improve analytical capabilities.
• Lead innovation through research, experimentation, and prototyping; build and scale “AI‑native” components such as models, agents, and reasoning layers.
• Build a portfolio that includes core ML/optimization models (forecasting, bias correction, replenishment, shrink/markdown, execution signals), agentic AI frameworks (human‑in‑the‑loop where needed, autonomous where safe/valuable), and LLM‑powered explanation and decision support.
• Lead cross‑functional partnerships with product, engineering, and domain experts to deliver analytical, predictive, and decision‑support solutions for supply chain and fulfillment functions.
• Recruit, build, and lead high‑performing teams of data scientists; provide technical and thought leadership, mentorship, and guidance.
• Use machine learning and AI to solve real customer problems, power the next generation experiences for large‑scale applications in real time, and drive breakthrough benefits to customers using personalized, enriched, and derived data from a range of sources.
• Prioritize projects across the team, allocate resources to meet business and team goals, and communicate sophisticated machine learning and modeling solutions effectively with intuitive visualizations for business stakeholders.

• Advanced degree in a STEM field (CS, DS, Engineering, Statistics, Math, etc.) preferred;
  PhD strongly preferred in Computer Science, Machine Learning, Statistics, Operations Research, Applied Mathematics, Industrial Engineering, or a closely related quantitative field.
• 12+ years of industry experience with 8+ years applying data science (experimental design, machine learning, deep learning, operations research, optimization) at scale.
• Proven track record of leading data science projects and teams to architect and deploy solutions at scale.
• Experience in the retail and grocery industry preferred, including supply chain optimization, merchandising, pricing, digital/e‑commerce, and customer/marketing analytics.
• Product thinking, deep expertise in systems design, and understanding of planning and operations from a data science perspective.
• Consulting experience and service mindset; comfortable interacting with scientists, engineers, product, and business clients.
• Business acumen and retail understanding; ability to set vision and guide teams through unstructured technical problems to deliver business impact.
• Proficient in Python and SQL; 5+ years of hands‑on experience building data science solutions and production‑ready systems on big data platforms (Snowflake, Spark, Hadoop). 5+ 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 diverse audiences.
• Experience with logistics and supply chain management systems;
  Snowflake and Azure Databricks experience is a plus.

• Machine learning, deep learning, applied optimization, and operations research.
• Python, SQL, AI/machine learning…