Principal Data Scientist - 11771

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 & Strategic Leadership: Define and drive the long-term technical roadmap for applied Machine Learning within the Supply Chain and Sourcing domains, ensuring alignment with broad product and engineering goals. Hybrid ML/OR Systems: Partner deeply with the Operations Research team. Design and build robust predictive models (e.g., demand forecasting, lead-time estimation, supplier risk scoring) that act as direct, high-quality inputs into downstream mathematical optimization algorithms. Advanced Model Development: Research, prototype, and deploy state-of-the-art machine learning models (Time Series Forecasting, Deep Learning, NLP, and GenAI) to extract actionable insights from massive, messy, and complex global supply chain datasets. MLOps & Scalability: Architect highly scalable data pipelines and ML infrastructure. Work closely with software engineers to deploy models into a high-throughput, multi-tenant SaaS environment, ensuring robust model monitoring and lifecycle management. Mentorship & Culture: Serve as the chief technical mentor for the Data Science team. Elevate the team's capabilities through rigorous code reviews, algorithmic design sessions, and championing best practices in data science. Cross-Functional Influence: Translate highly ambiguous business problems into structured data science projects. Communicate complex ML concepts, limitations, and trade-offs to non-technical executives, product managers, and key enterprise customers.

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 machine learning frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch, XGBoost). Production-grade programming proficiency in Python, as well as advanced skills in SQL and data manipulation. Proven track record of leading complex, multi-quarter ML initiatives and successfully deploying models that drive measurable business impact. Strong architectural understanding of integrating Machine Learning systems with downstream software services. Excellent executive-level communication skills, with the ability to explain complex statistical concepts to diverse audiences. You are a deeply technical ML expert who thinks holistically about end-to-end AI systems, particularly how predictive signals can drive downstream decision-making and optimization. You are comfortable operating in a high-autonomy environment, taking concepts from white-board theory to production-grade software. You are a force multiplier who loves mentoring others and elevating the engineering and scientific rigor of the entire organization. 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) Domain expertise in Supply Chain Management, Logistics, Demand Planning, or Strategic Sourcing. Familiarity with Operations Research concepts (e.g., Linear Programming, Heuristics) and experience building hybrid systems where ML outputs feed into optimization solvers (like Gurobi or CPLEX). Experience with Big Data technologies and distributed computing frameworks in the Hadoop/Spark ecosystem (e.g., PySpark, Databricks). Hands-on experience with cloud-native ML infrastructure (AWS SageMaker, Bedrock, containerization via Docker/Kubernetes). Experience applying Large Language Models (LLMs) and Generative AI to unstructured enterprise data.

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