Sr Principal Data Scientist

About this role

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Skills you'll use

What you'll do

• Set the technical direction for how Smartsheet uses applied ML and agentic AI across the customer lifecycle — what gets built, what doesn’t, and what good looks like
• Architect the systems behind the sub-agents: how they ground themselves in evidence, how risk and confidence are calibrated, how decisions are evaluated, and how all of it stays safe and trustworthy at scale
• Define the modeling, evaluation, and experimentation standards the team follows — from offline metrics to online rollout to production monitoring
• Personally ship the highest-leverage models and sub-agents — the ones that need a senior IC to frame the problem, push through ambiguity, and de-risk the approach
• Drive technical decisions on the data foundations and knowledge layer sub-agents reason over, with strong stewardship of privacy, aggregation, and customer trust
• Partner with senior Product, Engineering, and Applied AI leaders to shape strategy, roadmap, and investment
• Raise the bar for the data science team — mentor staff and principal data scientists, review designs, set hiring standards, and grow the org’s modeling rigor
• Bachelor’s degree and 12+ years of experience (or 14+ years of experience); advanced degree in a quantitative field (Statistics, CS, ML, Economics, Operations Research, or similar) strongly preferred
• Track record of setting technical direction for applied ML and AI work that ships and matters — major initiatives, complex systems, or new modeling paradigms taken from idea to production impact
• Deep applied ML expertise across both traditional ML and deep learning: gradient boosting, regularized linear models, transformer-based sequence models, foundation model embeddings, causal ML, contextual bandits, and offline RL

Who can apply

• Track record of setting technical direction for applied ML and AI work that ships and matters — major initiatives, complex systems, or new modeling paradigms taken from idea to production impact
• Deep applied ML expertise across both traditional ML and deep learning: gradient boosting, regularized linear models, transformer-based sequence models, foundation model embeddings, causal ML, contextual bandits, and offline RL
• Architect-level grasp of agentic AI systems: tool use, retrieval, multi-step reasoning, evaluation, guardrails, and the patterns for keeping all of it reliable in production
• Strong grasp of causal inference for intervention design and lifecycle modeling: uplift modeling, difference-in-differences, propensity scoring, and synthetic control
• Solid foundation in statistics and experimental design at scale: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods
• Experience operating ML in production at scale — feature engineering and pipelines, model monitoring, drift detection, retraining cadence, and the trade-offs between batch and real-time serving
• Proficient in SQL and Python; comfort with ML/LLM tooling at scale (Spark, Databricks, Snowflake, or equivalents), ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM), and visualization tools (Tableau or similar)
• Experience leading lifecycle modeling work — churn, expansion, adoption, plan health, lead/account scoring — and business fluency in the SaaS metrics that drive it (NRR, GRR, ARR, and cohort economics)

About Smartsheet

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