Develop and deploy machine learning and statistical models to support distribution center operations, logistics performance, wholesale customer service, and sourcing analytics.
Analyze large-scale operational data related to throughput, service levels, lead times, costs, and supplier performance.
Partner with global Distribution, Logistics, Customer Operations (Wholesale), and Sourcing teams to translate business problems into scalable analytics solutions.
Collaborate with data engineering teams to ensure clean, reliable, and well-structured analytical datasets.
Build dashboards, analytical tools, and decision-support outputs using Python, SQL, and Power BI.
Communicate insights and recommendations clearly to technical and non-technical stakeholders to drive adoption and action.
Support experimentation and delivery of GenAI-enabled use cases, such as natural language insights, automated analysis, operational summaries, and AI-assisted decision support.

Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, Supply Chain, or a related field.
2–4 years of experience in data science, advanced analytics, or applied machine learning roles.
Strong programming skills in Python and proficiency in SQL.
Experience with machine learning libraries such as scikit-learn, XGBoost, TensorFlow, or similar.
Strong analytical, problem-solving, and communication skills.
Experience working with distribution, logistics, wholesale customer operations, or sourcing data (Nice to Have).
Familiarity with supply chain systems such as WMS, TMS, ERP, or sourcing/procurement platforms (Nice to Have).
Experience with Power BI or similar data visualization tools (Nice to Have).

Data Scientist

Key skills