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Job Description Summary
This role will bring advanced technology, operations research, and analytical skills to unlock new and deeper insights at Schnucks. We are looking for a hands-on Data Scientist with a strong background in both Machine Learning and Optimization to design, build, and deploy scalable solutions across our business. You will work with business and engineering partners to translate retail challenges-such as supply chain scheduling, inventory optimization, and customer loyalty-into measurable AI/ML services, including GenAI agents, production ML models, and simulation frameworks.
Location:
St. Louis, Missouri.
Work Policy:
This role requires 4 days in office for up to 6 months to learn the role, then shifts to a hybrid model (3 days in office / 2 days remote).
What You'll Do (Essential Job Responsibilities) Strategic Optimization & Modeling: Design, develop, and deploy advanced methodologies such as Mixed-Integer Programming (MIP) models, metaheuristics (e.g., Large Neighborhood Search) and greedy priority-based scoring to optimize complex scheduling, supply chain, and warehouse planning functions. Advanced AI/ML Development: Design, train, and validate models for classification, regression, and time-series forecasting to evaluate strategic effectiveness around sales, gross profit, and basket-level analysis. GenAI Innovation: Develop and evaluate Generative AI agent applications using frameworks (e.g., Google ADK), leveraging techniques such as RAG and prompt engineering to improve internal decision-making. End-to-End Engineering: Build and operate reliable data pipelines and model inference endpoints across cloud platforms (GCP), ensuring models are integrated into production environments. Centralize & Automate: Standardize and automate key analytics functions currently spread across multiple teams to drive efficiency and best practices across the analyst organization. Stakeholder Partnership: Report findings and work closely with strategic and tactical decision-makers to powerfully convey recommendations and gain action on insights. Documentation & Best Practices: Write clear model documentation (step by step replication guide, problem formulation, modeling approach, validation) and advocate for coding best practices and reproducibility. Required Qualifications
Education: Master's Degree or Ph.D. in Operations Research, Industrial Engineering, Data Science, Computer Science, Applied Mathematics/Statistics, or a related quantitative field. Experience: 3 to 5 years of professional experience leading complex projects in optimization, simulation, or machine learning. Optimization Skills: Proven experience implementing metaheuristics (LNS, Simulated Annealing) and exact solvers (Gurobi) for large-scale combinatorial problems. Programming & Data: Strong Python and SQL skills. Proficiency in Python, including experience with subprocess orchestration, multiprocessing, and libraries like scikit-learn, pandas and numpy. Cloud & Deployment: Hands-on experience with Google Cloud Platform (GCP), specifically Vertex AI, BigQuery, and Cloud Storage for deploying production-grade AI/ML models. Statistical Knowledge: Solid comprehension of AI/ML algorithms, including their intuition, assumptions, and computational complexity. Communication & Collaboration: Demonstrated ability to work transparently within a team, providing regular technical updates and avoiding "black box" development styles. Preferred Knowledge, Skills, and Abilities
GenAI/LLM: Agentic Workflows: Experience building RAG (Retrieval-Augmented Generation) applications and advanced prompt engineering for multi-modal LLMs Visualization: Proficiency with Plotly Dash and Flask for building real-time, callback-driven web interfaces for workforce or task management. CI/CD Proficiency: Experience with GitHub Actions and Docker for containerizing and automating the deployment of data science applications to Google Cloud Run. Physical Requirements and Work Environment
Traditional office environment with computer equipment. Walking/Standing: Less than ½ day. Lifting: Up to 25 lbs. Travel: 0-25%. Additional Requirements - Internal Candidates:
Store/Facility Teammates: minimum of six months employment with Schnucks preferred. Store Support Center Teammates: minimum of one year employment with Schnucks preferred. Must be in good standing (not on a performance improvement plan or active discipline). If invited for an interview, must have manager approval based on performance.
Schnucks is an Equal Opportunity Employer.
Sprachkenntnisse
- English
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