O'Reilly Auto Parts
Sr. Manager, Data Science & Applied AI
- Compensation
- Not disclosed
- Location
- Headquarters
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Proven leadership experience managing Data Science, Machine Learning, or Applied AI teams, preferably across multiple business domains.
- Strong expertise in Machine Learning, Applied AI, Generative AI, optimization, predictive modeling, and advanced analytics.
- Hands-on understanding of modern GenAI architectures, including LLMs, RAG, embeddings/vector search, AI agents, prompt engineering, model evaluation, guardrails, and LLMOps.
- Strong experience with enterprise cloud AI platforms, preferably GCP and Vertex AI.
- Experience designing and operationalizing scalable MLOps/LLMOps architectures and production AI solutions.
- Demonstrated ability to connect AI/ML initiatives to measurable operational and financial outcomes.
- Strong understanding of data engineering, data quality, governance, security, and enterprise data architecture required to support AI at scale.
- Proven ability to influence senior executives and translate ambiguous business challenges into a prioritized portfolio of Data Science and AI initiatives.
- Strong people leadership experience, including hiring, developing, coaching, and retaining Data Science and AI talent.
- Excellent executive communication, storytelling, stakeholder management, and organizational leadership skills.
Nice to have
- Retail industry experience, particularly within large-scale, multi-channel or store-based retail environments.
- Deep business understanding of Inventory Management, Inventory Optimization, Demand Forecasting, Replenishment, Supply Chain, Distribution, and Store Operations.
- Experience applying AI/ML to retail use cases such as demand forecasting, inventory optimization, assortment, pricing, workforce optimization, customer personalization, and operational decision-making
- Experience leading People Analytics/Data Science initiatives such as workforce planning, retention, engagement, labor optimization, and talent analytics.
- Experience delivering Generative AI and Agentic AI solutions from experimentation through production.
- Experience driving organizational adoption and change management around AI-enabled ways of working.
- Experience partnering with Product, Engineering, Data, and Business organizations to move AI solutions from POC to production and measurable business value.
Full description
The Sr. Manager, Data Science & Applied AI is a strategic and technical leader responsible for leading Data Science and Applied AI capabilities across multiple business domains, including People Analytics, Inventory Optimization, Supply Chain, Operations, and Generative AI.
This leader will manage and develop high-performing Data Science teams while establishing the strategy and technical direction for Machine Learning, Applied AI, Generative AI, and advanced analytics solutions. The role partners closely with business, product, data engineering, architecture, and technology leaders to translate complex business opportunities into scalable AI-driven solutions with measurable business outcomes.
The ideal candidate combines strong AI/ML and GenAI technical depth with retail business acumen, particularly across Inventory, Supply Chain, Store Operations, Merchandising, Workforce/People Analytics, and other operational functions.
This is an on-site position located in Springfield, MO. Remote work is not an option for this role. Key Responsibilities
- Lead multiple Data Science and Applied AI teams supporting business domains such as People Analytics, Inventory Optimization, Supply Chain, Operations, and Generative AI.
- Define and execute the enterprise strategy for Applied AI, Machine Learning, Generative AI, predictive analytics, and optimization across supported business domains.
- Identify high-value business opportunities where AI can improve inventory availability, forecasting, replenishment, supply chain efficiency, workforce effectiveness, operational productivity, customer experience, and decision-making.
- Drive the development and productionization of GenAI solutions, including enterprise copilots, intelligent assistants, RAG-based applications, agentic AI workflows, natural-language analytics, and knowledge-driven automation.
- Establish standards for LLM evaluation, grounding, guardrails, responsible AI, security, observability, model monitoring, and human-in-the-loop controls.
- Partner with Data Engineering, Architecture, and Platform teams to establish scalable MLOps and LLMOps capabilities using GCP, Vertex AI, and enterprise data platforms.
- Lead advanced Data Science capabilities including forecasting, optimization, recommendation systems, predictive modeling, experimentation, segmentation, anomaly detection, and simulation/What-If modeling.
- Ensure AI/ML solutions are built with production-grade engineering standards, including scalability, reliability, monitoring, data quality, automated testing, reproducibility, and lifecycle management.
- Establish measurable KPIs and ROI frameworks that connect model performance to business outcomes and financial value.
- Translate complex model outputs and AI capabilities into actionable recommendations and compelling narratives for executive and business leadership.