Principal Data Scientist

Autodesk•San Francisco, CA

9d•$135,000 - $242,000

About The Position

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.

Requirements

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 nondeterministic 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-functional teams

Responsibilities

Design and implement predictive models to analyze and anticipate user behavior, intent, and outcomes across multi-agent workflows
Define and establish data instrumentation, telemetry, and observability standards for agent-based systems and user interactions
Develop frameworks and prototypes for analyzing and optimizing non-deterministic user experiences (including user interfaces) driven by AI agents
Collaborate with product and engineering teams to integrate predictive intelligence into agent orchestration and decision-making systems
Create analytical models and reporting frameworks to generate business intelligence, forecasting, and performance insights
Guide experimentation strategies, including A/B testing, causal inference, and evaluation methodologies for agent performance
Provide technical leadership and recommendations on data architecture, model selection, and system scalability
Translate ambiguous, early-stage product questions into structured analytical programs with clear hypotheses, methods, and business impact