Sr Lead Data Scientist - 11773

Opportunity

What you’ll do

• Own complex ML features end to end, designing, prototyping, and implementing predictive models for supply chain and sourcing problems.
• Coordinate with Operations Research so forecasts and estimates can serve as calibrated inputs to large-scale optimization engines.
• Partner with software architects on scalable, automated ML pipelines and drive MLOps for deployment, monitoring, and retraining in a multi-tenant SaaS platform.
• Mentor data scientists, set code review standards, lead system design sessions, and strengthen statistical and engineering practices.
• Collaborate with product and engineering leaders on requirements, ML specifications, and algorithmic trade-offs.
• Lead feature engineering on complex datasets and identify new data signals to improve model accuracy.

Qualifications

• Master’s degree or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or a closely related quantitative field.
• 8+ years applying machine-learning methods and deploying production models in commercial software environments.
• Expertise in predictive modeling, time-series forecasting, and modern ML frameworks such as Scikit-learn, PyTorch, TensorFlow, or XGBoost.
• Production-grade Python, software design patterns, object-oriented programming, and CI/CD; expert SQL and large-dataset feature engineering.
• Strong communication skills to explain complex statistical behavior and model trade-offs to product and business leaders.
• Supply chain domain expertise; Operations Research and solver experience; cloud-native ML, distributed computing, and LLM experience are preferred.

Technical skills

• Required domains include machine learning, predictive modeling, time-series forecasting, NLP, deep learning, classification, and MLOps.
• Preferred tools and platforms include Gurobi, CPLEX, AWS SageMaker, GCP, Docker, Kubernetes, PySpark, and Databricks.