Sr Lead Data Scientist

Job Summary

Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins.

Responsibilities

• Technical Execution Ownership: Take end-to-end technical ownership of complex, high-impact machine learning features. Design, prototype, and implement advanced predictive models (Time Series, NLP, Deep Learning, Classification) to solve highly ambiguous supply chain and sourcing challenges.
• ML or Synergy: Act as the primary technical liaison to the Operations Research team. Architect machine learning solutions whose outputs (e.g., probabilistic forecasts, lead-time estimates) serve as high-quality, calibrated inputs for large-scale mathematical optimization engines.
• Architecture Scalability (MLOps): Partner with software architects to design highly scalable, automated machine learning pipelines. Drive the adoption of robust MLOps practices, ensuring models are efficiently deployed, monitored, and retrained within Coupa's multi-tenant SaaS platform.
• Technical Mentorship: Act as a force multiplier by mentoring senior and junior data scientists. Establish rigorous code review standards, lead system design sessions, and elevate the team's statistical and engineering practices.
• Cross-Functional Collaboration: Work closely with Product Managers and Engineering leaders to define feature requirements, translate business objectives into technical ML specifications, and manage algorithmic trade-offs.
• Data Strategy: Lead efforts to engineer novel features from massive, complex, and messy real-world datasets, continuously identifying new internal and external data signals to improve model accuracy.

What You Will Bring to Coupa

• Master's degree or Ph. D. in Data Science, Computer Science, Statistics, Mathematics, or a closely related quantitative field.
• 8+ years of industry experience applying machine learning methods and deploying production-level models in commercial software environments.
• Expert knowledge of predictive modeling, time-series forecasting, and modern machine learning frameworks (e.g., Scikit-Learn, PyTorch, TensorFlow, XGBoost).
• Strong, production-grade programming proficiency in Python, with a solid understanding of software design patterns, object-oriented programming, and CI/CD best practices.
• Expert-level SQL skills and extensive experience with data manipulation and feature engineering on large datasets.
• Excellent communication skills, with a proven ability to explain highly complex statistical behaviors and model trade-offs to product and business leaders
• You are an elite machine learning practitioner who also loves writing clean, production-grade, scalable code.
• You are highly analytical, self-driven, and excel at converting ambiguous, high-level business goals into structured, elegant data science solutions.
• You are a natural technical leader who enjoys mentoring others and is passionate about cultivating a rigorous, collaborative, and fast-paced engineering culture.
• You embody Coupa's core values: Ensure Customer Success, Focus on Results, and Strive for Excellence.

What Will Give You a Competitive Edge (Preferred Qualifications)

• Deep domain expertise in Supply Chain Network Design, Demand Planning, Strategic Procurement, or Logistics.
• Familiarity with Operations Research concepts (e.g., Linear Programming, MIP) and experience building predictive models that interact with commercial solvers (such as Gurobi or CPLEX).
• Hands-on experience developing and deploying ML microservices on cloud-native platforms (AWS SageMaker, GCP, Docker, Kubernetes).
• Extensive experience with distributed computing frameworks (e.g., PySpark, Databricks) for processing large-scale datasets.
• Experience integrating Large Language Models (LLMs) to extract features from unstructured enterprise text data.

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