Sr. Data Scientist II (Remote Eligible)

Smartsheet is looking for an experienced Senior Data Scientist II to build the ML models and AI sub-agents that drive growth, monetization, efficiency, and retention across the customer lifecycle. You’ll work end-to-end framing problems, building models across the modern ML and deep learning toolkit, designing sub-agents that reason and act, and shipping all of it into production for millions of users. The data is unusually rich: petabyte-scale execution data spanning two decades of how real work gets done. You are curious, technically rigorous, and can translate complex modeling and sub-agent behavior into clear recommendations for your partners. You will work primarily with Product and Engineering and will be a part of Smartsheet’s Business Intelligence team.

This full-time position initially reports to the VP of Data Science located in our Bellevue, WA office, or you may work remotely from anywhere in the US where Smartsheet is a registered employer.

You Will:

• Design and ship AI sub-agents that act across the customer lifecycle, combining predictive models, retrieved context, and LLM reasoning to recommend or take action
• Build the predictive and prescriptive models that power those sub-agents  churn risk, growth, adoption trajectories, account health scoring, and similar lifecycle problems
• Develop the data foundations and knowledge layer those sub-agents reason over, applying responsible aggregation and privacy-aware design
• Design the tools, retrieval, and grounding strategies each sub-agent uses; decide when a sub-agent should act, recommend, defer, or escalate
• Build the evaluation harnesses that determine when a sub-agent is good enough to ship and that catch regressions in production
• Define metrics and experimentation strategy for sub-agent rollouts; measure real customer impact, not just offline accuracy or eval scores
• Partner with Product, Engineering, and Applied AI teams from problem framing through production deployment
• Drive a data and modeling culture within Product and Engineering, and mentor other data scientists on the team

You Have:

• Bachelor’s degree and 8+ years of experience (or 10+ years of experience); advanced degree in a quantitative field (Statistics, CS, ML, Economics, Operations Research, or similar) preferred
• 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
• 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: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods
• Hands-on experience taking LLM- and agent-based systems to production: tool use, retrieval, multi-step reasoning, evaluation, and guardrails
• Experience operating ML in production — 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 modeling the customer lifecycle — churn, expansion, adoption, plan health, lead/account scoring — and business fluency in the SaaS metrics that drive it (NRR, GRR, ARR, and cohort economics)
• A pragmatic production bar: latency, cost, monitoring, drift, hallucination, and what happens when the model or sub-agent is wrong
• Strong track record of forming effective cross-functional partnerships and communicating analysis clearly to technical and executive audiences
• Ability to research and learn new technologies, tools, and methodologies, and to thrive in a dynamic environment — finding opportunities and executing in both independent and collaborative environments