Supply Chain Data Scientist

Job description

This role has been designed as ‘Hybrid’ with an expectation that you will work on average 2 days per week from an HPE office. **Who We Are** Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. **Job Description** ****HPE Operations**** is our innovative IT services organization. It provides the expertise to advise, integrate, and accelerate our customers’ outcomes from their digital transformation. Our teams collaborate to transform insight into innovation. In today’s fast-paced, hybrid IT world, being at business speed means overcoming IT complexity to match the speed of actions to the speed of opportunities. Deploy the right technology to respond quickly to market possibilities. Join us and redefine what’s next for you. **Job Family Definition** Designs, develops and applies programs, methodologies and systems based on advanced analytic models (e.g. predictive analytics, optimization, artificial intelligence) to transform structured and unstructured data into meaningful and actionable information insights that drive decision making. Uses visualization techniques to translate analytic insights into understandable business stories (e.g., predictive analytics, optimization, artificial intelligence). Embeds analytics into client’s business processes and applications. Combines business acumen and scientific methods to solve business problems. **Management Level Definition** Contributions include applying an intermediate level of subject matter expertise to solve common technical problems. Acts as an informed team member providing analysis of information and recommendations for appropriate action. Works independently within an established framework and with moderate supervision. **What You'll Do** The Supply Chain Data Scientist is responsible for designing and deploying advanced analytics and AI-driven solutions that enhance visibility, decision-making, and operational efficiency across Supply Chain Planning and Operations (SCPO) and Business Units. This role combines predictive analytics, machine learning, and Generative AI techniques to transform structured and unstructured data into scalable intelligence products. The individual will develop predictive models for supply chain insights (e.g., demand signals, risk prediction, lifecycle analytics) while also building AI-powered copilots, Retrieval-Augmented Generation (RAG) systems, and intelligent knowledge assistants to enhance business process. The Supply Chain Data Scientist plays a critical role in advancing SCPO’s analytics and AI capabilities by combining classical data science methodologies with modern Generative AI systems. **Responsibilities** - Applies basic knowledge of the client's business needs to formulate and define analytic objectives. Uses available data elements, defines business rules, and solution objectives. - Designs, develops, and applies programs, methodologies, and systems based on advanced analytics models (e.g., predictive analytics, optimization, and artificial intelligence) to transform structured and unstructured data into meaningful and actionable insights that drive decision-making. - Builds models to support/contribute to the overall solution, validates the initial model, and validates results & performance after the implementation. - Researches, identifies, and aids in machine learning and artificial intelligence solutions to problem domains. Contributes significantly to the measurement of business performance based on the model deployed. If needed, leads the model enhancements. - Create visualization of the model's insights for easy consumption **What You Need To Bring** ***Education and Experience Required:*** - Master´s degree in Statistics, Operations Research, Computer Science, Artificial Intelligence, or equivalent preferred. Or a bachelor´s degree in these areas and at least 2-3 years of relevant experience. **Knowledge And Skills** Advanced Analytics and Machine Learning - Predictive modelling and statistical analysis - Optimization and time series analysis - Feature engineering and model validation - ML life cycle Generative AL & LLM Engineering - Retrieval Augmented Generation (RAG) systems - AI copilots and agent-based workflows - Model Context Protocol (MCP) integration - Prompt engineering and evaluation frameworks - LLM orchestration frameworks (LangChain, LangGraph, etc.) Data Engineering & Platforms - ML ecosystem (Python, Pandas, Scikit-learn, SQL) - Distributed data platforms (Databricks, PySpark) - Vector databases and semantic search Visualization and Communication - Data storytelling and executive level presentations - Dashboarding tools (Power BI, Tableau) - Translating analytics insights into operational actions **Other** - Solid communication and presentation skills. - Strong interpersonal skills and effectiveness in working across geographical boundaries. **Additional Skills** Accountability, Accountability, Action Planning, Active Learning, Active Listening, Agile Methodology, Agile Scrum Development, Analytical Thinking, Bias, Coaching, Creativity, Critical Thinking, Cross-Functional Teamwork, Data Analysis Management, Data Collection Management (Inactive), Data Controls, Design, Design Thinking, Empathy, Follow-Through, Group Problem Solving, Growth Minds