Data Scientist

Job Title: Supply Chain Lead Data Scientist Location: USA OR Canada (Remote) Manager’s input: Lead Data Scientist contractor. We need a very seasoned person with specific supply chain data science experience, who can drive work independently and collaborate with multiple Supply Chain engineering teams on both traditional ML/forecasting and GenAI application development. The hiring window is now through EOY - getting the right person is the most important aspect. We’re open to both nearshore and onshore. Do you have anyone who fits this need? Job Overview: We are seeking a highly experienced and results-oriented Supply Chain Lead Data Scientist to drive innovation and optimization within Sephora's supply chain. In this role, you will lead the application of advanced data science and machine learning techniques to solve complex supply chain challenges, improve efficiency, and enhance decision-making. You will work independently while collaborating closely with various Supply Chain engineering teams to develop and deploy impactful solutions. This role offers a unique opportunity to shape our supply chain strategy through cutting-edge AI and data-driven approaches. Key Responsibilities: Lead the design, development, and deployment of advanced machine learning models and algorithms specifically for supply chain applications (e.g., demand forecasting for special events and new SKU introductions, inventory optimization, network design, logistics optimization). Collaborate with Supply Chain engineering teams to understand their data requirements, business processes, and technical constraints. Independently manage end-to-end data science projects, from problem definition and data exploration to model deployment and performance monitoring. Apply expertise in Generative AI to build applications leveraging AI services Apply strong expertise in time-series analysis, forecasting, and statistical modeling techniques relevant to supply chain and external data sources. Conduct in-depth data analysis to identify trends, patterns, and opportunities for improvement across the supply chain. Communicate findings, insights, and technical concepts to both technical and non-technical audiences through clear visualizations and presentations. Stay at the forefront of AI research and supply chain trends, experimenting with emerging techniques and staying informed about the latest advancements in the field. Lead the design and implementation of end-to-end solutions for batch and real-time algorithms along with tooling around monitoring, logging, automated testing, performance testing, and A/B testing of algorithms. Qualifications and Skills: Advanced degree (Master's or Ph.D.) in Computer Science, Statistics, Data Science, Supply Chain Management, or a related field. 8+ years of experience in data science and machine learning, with a strong focus on supply chain applications. 6+ years of experience working with cloud computing platforms (AWS, Azure, GCP), distributed computing (Spark), and ML tooling such as MLFlow. 8+ years of experience working with a variety of relational SQL and NoSQL databases Mastery in programming languages such as Python, R, Scala, or Rust and proven experience with machine learning libraries and frameworks. Strong understanding of deep learning frameworks (e.g., TensorFlow, PyTorch) and hands-on experience in implementing complex AI models. Deep understanding of supply chain principles, processes, and data. Proven track record of successfully deploying data science solutions to improve supply chain performance. Excellent problem-solving skills, analytical thinking, and the ability to approach complex challenges creatively. Strong verbal and written communication skills to effectively collaborate with cross-functional teams and convey technical concepts to non-technical stakeholders. Passion for staying updated with industry trends and sharing knowledge within the team. Practical experience leveraging both GenAI services and open source models. Ability to work independently and drive projects to completion with minimal supervision. Experience working in a fast-paced, dynamic environment.