Principal Data Scientist

Principal Data ScientistAt 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 curious, adaptable, customer-focused, and excited to help shape the future of work at Autodesk.ResponsibilitiesDesign and implement predictive models to analyze and anticipate user behavior, intent, and outcomes across multi-agent workflowsDefine and establish data instrumentation, telemetry, and observability standards for agent-based systems and user interactionsDevelop frameworks and prototypes for analyzing and optimizing non-deterministic user experiences (including user interfaces) driven by AI agentsCollaborate with product and engineering teams to integrate predictive intelligence into agent orchestration and decision-making systemsCreate analytical models and reporting frameworks to generate business intelligence, forecasting, and performance insightsGuide experimentation strategies, including A/B testing, causal inference, and evaluation methodologies for agent performanceProvide technical leadership and recommendations on data architecture, model selection, and system scalabilityTranslate ambiguous, early-stage product questions into structured analytical programs with clear hypotheses, methods, and business impactMinimum Qualifications8+ years in data science or applied ML, with significant time in product analytics or user behavior modelingDeep experience with predictive modeling — classification, survival analysis, sequence models, or LTV/propensity frameworksFluency in designing instrumentation and event schemas for complex, stateful systemsDemonstrated ability to define metrics and measurement frameworks for new product spacesExperience working on or adjacent to AI/ML-powered products — especially those with nondeterministic outputsExperience with LLMs and related technologies, including frameworks, embedding models, vector databases, and Retrieval-Augmented Generation (RAG) systems, in production settingsDeep understanding of data modeling, system architectures, and processing techniques, including 2D/3D geometric data representationsProven ability to translate theoretical concepts into practical solutions and prototype implementationsAbility to work autonomously while effectively collaborating across teams, bridging the gap between research and practical implementationExcellent technical writing and communication skills for documentation, presentations, and influencing cross-functional teams