Principal Data Scientist

Job Requisition ID # 26WD98421 Position Overview At Autodesk, we do what no other company can: we help our customers design and make anything. The Experience Foundations team at Autodesk plays a critical role in designing the experiences that make that mission a reality, especially in this transformative moment where seamless digital experiences and AI-powered innovation will empower customers and teams to achieve meaningful outcomes faster. The Principal Data Scientist will work within an Engineering team focused on building frameworks to evaluate AI Systems. This is a critical data science role for our agentic product platform—a next-generation framework that combines domain-specific AI agents into a unified harness enabling complex, multi-step, personalized work across product surfaces. As Principal Data Scientist, you will help build fundamental evaluation techniques, create algorithms to assess quality scores, as well as define and predict what success for users and the product looks like. You'll work at the intersection of predictive modeling, behavioral analytics, and AI system design — partnering closely with product, engineering, and platform teams to build the measurement and intelligence tools and where appropriate, infrastructure, that this product needs to learn, adapt, and grow. To deliver our vision, we need exceptional contributors and leaders who are curi [... source excerpt omitted ...] ection, and system scalability Translate ambiguous, early-stage product questions into structured analytical programs with clear hypotheses, methods, and business impact Minimum Qualifications 8+ years in data science or applied ML, with significant time in product analytics or user behavior modeling Deep experience with predictive modeling — classification, survival analysis, sequence models, or LTV/propensity frameworks Fluency in designing instrumentation and event schemas for complex, stateful systems Demonstrated ability to define metrics and measurement frameworks for new product spaces Experience working on or adjacent to AI/ML-powered products — especially those with non [... source excerpt omitted ...] eterministic outputs Experience with LLMs and related technologies, including frameworks, embedding models, vector databases, and Retrieval-Augmented Generation (RAG) systems, in production settings Deep understanding of data modeling, system architectures, and processing techniques, including 2D/3D geometric data representations Proven ability to translate theoretical concepts into practical solutions and prototype implementations Ability to work autonomously while effectively collaborating across teams, bridging the gap between research and practical implementation Excellent technical writing and communication skills for documentation, presentations, and influencing cross-fu