Principal Data Scientist - 11771

The opportunity

What you’ll do

• Define the long-term applied ML roadmap for supply chain and sourcing, aligned with product and engineering goals.
• Build predictive models with Operations Research teams, including demand forecasting, lead-time estimation, and supplier risk scoring for optimization algorithms.
• Research, prototype, and deploy models involving time-series forecasting, deep learning, NLP, and generative AI.
• Architect scalable data pipelines and ML infrastructure for a high-throughput, multi-tenant SaaS environment.
• Partner with software engineers on deployment, model monitoring, and lifecycle management.
• Mentor data scientists through code reviews, algorithmic design sessions, and best-practice development.
• Turn ambiguous business problems into structured data science projects and communicate ML trade-offs to executives, product managers, and enterprise customers.

What you will bring

• Ph.D. or master’s degree in data science, computer science, statistics, mathematics, or a closely related quantitative field.
• 8+ years of industry experience building and deploying machine learning models in a commercial software environment.
• Deep expertise in predictive modeling, time-series forecasting, statistical analysis, and modern ML frameworks.
• Production-grade Python proficiency, with advanced SQL and data manipulation skills.
• Track record leading complex, multi-quarter ML initiatives and deploying models with measurable business impact.
• Strong architectural understanding of ML system integration with downstream software services.
• Executive-level communication skills and ability to explain statistical concepts to diverse audiences.
• Comfort taking concepts from theory to production in a high-autonomy environment; expected to mentor and elevate team rigor.

Preferred qualifications

• Supply chain management, logistics, demand planning, or strategic sourcing domain expertise is preferred.
• Operations Research familiarity, including linear programming or heuristics, and experience feeding ML outputs to Gurobi, CPLEX, or other optimization solvers are preferred.
• Hadoop/Spark ecosystem experience, including PySpark or Databricks, is preferred.
• Cloud-native ML infrastructure experience, including AWS SageMaker, Bedrock, Docker, or Kubernetes, is preferred.
• The provided description cuts off mid-sentence while listing additional preferred experience with large language models and generative AI.

Work arrangement

• The description labels the role hybrid and identifies Bangalore, India.