In Short
As a Staff Data Scientist in Supply Chain, you will play a pivotal role in shaping the future of On’s supply chain. You will serve as a technical leader within our data science team, responsible for designing, developing, and deploying cutting-edge optimization solutions and advanced forecasting models. Your contributions will significantly impact our ability to optimize inventory, improve planning accuracy, and make data-driven decisions on a global scale.
Your role will involve tackling our most complex and ambiguous challenges in the supply chain. You will not only build and implement models but also provide strategic guidance, mentor fellow data scientists, and drive the technical roadmap for forecasting at On.
About the Team
You will be part of a growing and diverse team of ML engineers, data scientists, data engineers, and product managers who are passionate about revolutionizing how we leverage AI/ML to solve complex challenges across On. Our focus is on building and operating innovative and impactful models that personalize experiences, optimize decision-making, and predict future trends. The team operates in a fast-paced environment and is accustomed to rapid turnaround times and ambitious targets. Our shared goal is to ensure efficient growth at high speed, allowing our ML systems to scale with On's needs.
Your Mission
- Position data science to drive top-line impact on the supply chain, reducing stockouts and ensuring orders are met fully, while providing clarity on technological, algorithmic, and process-related actions needed to achieve results.
- Design, develop, and implement state-of-the-art optimization models to create a more efficient, resilient, and reliable supply chain.
- Lead complex, end-to-end optimization projects from conception to deployment, ensuring they align with business needs and are both scalable and robust.
- Provide guidance on forecasting methodologies and their implications for supply chain optimization and performance.
- Collaborate with senior stakeholders across the organization, including Demand Planning, Supply Planning, Product, and Controlling, to inform supply chain strategies that capitalize on data and AI.
- Act as a thought leader in supply chain optimization and forecasting, remaining abreast of the latest research and technologies to identify opportunities for application at On.
- Mentor and coach fellow data scientists, providing technical guidance to support their career growth.
- Contribute to the development of our data science platform and infrastructure, ensuring we have the tools and processes to efficiently build and deploy models.
Your Story
- You possess a deep understanding of statistical modeling, machine learning, and time-series forecasting, with over 8 years of experience in data science focused on optimization within supply chains. Experience in building forecasting models is highly desirable.
- Your educational background includes a Master’s or Ph.D. in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.
- You are a hands-on data scientist with strategic thinking abilities, capable of leading projects, mentoring others, and driving technical decisions.
- You have expertise in Python and SQL, with experience using data science libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow/PyTorch, and you are familiar with deploying and maintaining machine learning and optimization models in production environments.
- You have experience with optimization software such as Gurobi and possess fluency in large-scale data processing and distributed computing frameworks (e.g., Spark).
- As a strong communicator, you can build relationships with stakeholders at all levels of the organization, translating complex technical concepts into business terms and fostering alignment around your proposed solutions.
- You are passionate about leveraging data to drive business impact and are enthusiastic about the opportunity to shape the future of On’s supply chain.
Apply online using the form below. Only applications matching the job profile will be considered.