Sr Lead Data Scientist - 11773

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.

Why join Coupa? Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend. Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence. Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other. Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa.

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.

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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