The posting
Responsibilities H-E-B’s Corporate Planning and Analysis Team develops and maintains budgets and financial systems while providing current, reliable financial data, analysis, and technical information. The AI Center of Excellence (AI COE) is a new team inside P&A standing up the P&A AI Portal — a governed, self-serve way for non-technical partners across H-E-B to ask business questions in plain English and get answers they can trust, grounded in certified data, with visibility into how the answer was produced. As a Decision Scientist, your archetype is a Business Decision Scientist. We’re looking for someone who can bridge data and business domain expertise — someone who can codify how the business actually makes decisions into reusable context, and help build the process and platform that let other analysts across H-E-B leverage and contribute to that context, so their own work moves faster. Once you’re eligible, you’ll become an Owner in the company, so we’re looking for commitment, hard work, and focus on quality and Customer service. “Partner-owned” means our most important resources — People — drive the innovation, growth, and success that make H-E-B The Greatest Omnichannel Retailing Company. Do you have a: HEART FOR PEOPLE… willingness to take a break from algorithmic thinking to translate and share your findings with a variety of business customers? HEAD FOR BUSINESS… skills to blend rigorous decision science with real business acumen? PASSION FOR RESULTS… drive to generate business-valued questions and evidence-based recommendations, not just models? We are looking for: - a decision scientist who can bridge data and business domain expertise, codify business context so it can be reused, and help build the process and platform that let other analysts across H-E-B leverage and contribute to that context to accelerate their own work What is the work? Works directly with business partners to understand how a decision actually gets made today, and codifies that judgment into reusable, shareable context — not a one-off analysis. Helps design and build the process and platform that let other analysts find, reuse, and contribute to that codified context, rather than everyone starting from scratch. Reasons about what a data-driven or AI-generated answer implies for the business, not just whether it’s technically correct — and can defend that reasoning to a business partner. Builds the feedback loop that turns real usage — what worked, what didn’t, what was missing — into concrete improvements to the shared context and platform. Applies causal and comparative reasoning to distinguish a real business signal from noise, seasonality, or a data-quality artifact before it reaches a partner as a recommendation. Helps set the standards for what “good” looks like as more analysts contribute — so the platform gets more useful, not less trustworthy, as it scales. What is your background? A related degree (statistics, economics, data science, operations research, or similar) or comparable formal training, certification, or work experience 3+ years of experience in a retail or retail-related decision science, analytics, or data science role Demonstrated ability to go from a business question to a shipped, defensible recommendation — not just a model or a dashboard Comfortable working with imperfect, incomplete, or newly-modeled data (serve-layer tables still in progress, sparsely-populated join keys, etc.) and saying clearly what is and isn’t known yet Do you have what it takes to be a Decision Scientist on the AI COE? Technical knowledge in SQL and Python; comfortable enough with data pipelines and applied AI tools to work alongside the people building them, without needing to build them yourself Working knowledge of causal reasoning, experiment design, or applied statistics — enough to question whether a surfaced pattern is a real driver or a spurious correlation Ability to work across multiple business domains without needing to become a deep specialist in each one first Ability as a creative storyteller and translator between business questions and technical capabilities Ability to define what “correct” or “good” looks like for a business question before anyone builds toward it Comfortable operating in a fast-moving build with a small team and shifting priorities Preferred (more technical background): Familiarity with back-end AI/ML platforms (e.g. Vertex AI, Databricks, or similar) — enough to understand what’s possible and speak the same language as the engineers building on them Comfortable working in a command-line environment (CLIs) as part of day-to-day work Experience building and deploying an application or tool end to end, even a small one — not just producing an analysis or notebook Exposure to version control (e.g. Git) and collaborative development workflows Can you… Work in a fast-paced retail environment with frequently shifting priorities Work extended hours; sit for long periods
