Sr Principal Data Scientist

About the role

Smartsheet is seeking a senior technical leader to guide the development of machine learning models and AI agents that optimize customer lifecycle, monetization, and retention. You will architect production-grade systems using two decades of proprietary execution data to drive strategy and technical standards across the organization. The role requires establishing the technical roadmap for applied ML and agentic AI initiatives while balancing innovation with reliability and scalability. You will define frameworks for sub-agent reasoning, evidence grounding, and reliability to ensure robust AI behavior in production environments. This position involves setting organizational standards for model evaluation, experimentation, and production monitoring to maintain best practices. You will build and deploy high-impact models that require senior-level problem framing and de-risking to align with business objectives. Collaboration with product and engineering leadership will ensure technical investments directly support overarching business goals and growth. Additionally, you will mentor staff and principal data scientists to improve design rigor, communication, and technical output across the team.

Key facts

What you'll do

• Establish the technical roadmap for applied ML and agentic AI initiatives, defining priorities aligned with customer lifecycle goals.
• Design frameworks for sub-agent reasoning, evidence grounding, and reliability to ensure consistent and verifiable AI outputs.
• Set organizational standards for model evaluation, experimentation, and production monitoring to maintain high-quality benchmarks.
• Build and deploy high-impact models that require senior-level problem framing, de-risking, and cross-functional coordination.
• Oversee the data foundations and knowledge layers while maintaining strict privacy and security protocols across all workflows.
• Collaborate with product and engineering leadership to align technical investments with business goals and strategic priorities.
• Mentor staff and principal data scientists to improve design rigor, technical execution, and ownership of complex problems.
• Define and implement strategies for optimizing customer retention, monetization, and lifecycle outcomes using data-driven approaches.
• Leverage two decades of proprietary execution data to identify patterns, insights, and opportunities for model-driven improvements.
• Lead investigations into causal inference methods such as uplift modeling, propensity scoring, and synthetic control to guide decision-making.
• Manage the end-to-end lifecycle of production ML systems, including feature pipelines, drift detection, and serving trade-offs.
• Maintain fluency in core SaaS metrics including NRR, GRR, ARR, and cohort analysis to guide model success criteria.
• Navigate ambiguity and influence senior stakeholders by translating technical concepts into actionable business strategies.
• Drive the adoption of AI agent frameworks, tool use, and multi-step reasoning to enhance automation and decision support.

Requirements

• Bachelor degree with 12+ years of experience, or 14+ years of experience without a degree; advanced degree in a quantitative field preferred.
• Proven history of taking complex ML or AI projects from concept to production in a fast-paced, high-impact environment.
• Expertise in traditional ML and deep learning, including transformer-based models, embeddings, and gradient boosting techniques.
• Experience building agentic AI systems, specifically regarding tool use, multi-step reasoning, and implementation of guardrails.
• Proficiency in causal inference methods like uplift modeling, propensity scoring, and synthetic control for rigorous analysis.
• Strong background in large-scale statistical design, including hypothesis testing and quasi-experimental methods to validate outcomes.
• Experience managing production ML lifecycles, including feature pipelines, drift detection, and evaluation of serving trade-offs.
• Business fluency in SaaS metrics such as NRR, GRR, ARR, and cohort analysis to guide model performance and business impact.
• Ability to navigate ambiguity and influence senior stakeholders through clear communication and data-backed recommendations.
• Commitment to maintaining privacy and security protocols in all stages of data handling and model deployment.

Skills & tools

• Python and SQL for data manipulation, analysis, and modeling tasks across the lifecycle.
• ML frameworks including PyTorch, scikit-learn, XGBoost, and LightGBM to build and optimize models.
• Data and ML infrastructure such as Spark, Databricks, and Snowflake for scalable processing and storage.
• Visualization tools like Tableau or similar platforms to communicate insights and model performance effectively.

Practical notes

Smartsheet is an equal opportunity employer and provides accommodations for the interview process upon request.

About the company

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