Sr. Data Scientist II

About the role

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 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 in production for millions of users. The role centers on building the predictive and prescriptive models that power those sub-agents, including churn risk, growth, adoption trajectories, account health scoring, and similar lifecycle problems. You will develop the data foundations and knowledge layer those sub-agents reason over, applying responsible aggregation and privacy-aware design while shaping the data strategy that underpins trustworthy AI. This is an end-to-end position where you frame problems, build models across the modern ML and deep learning toolkit, and partner closely with Product and Engineering to ship solutions that move business metrics. You will be a part of Smartsheet's Business Intelligence team and will help drive a data and modeling culture within Product and Engineering, mentoring other data scientists on the team along the way.

Key facts

What you'll do

• 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, addressing 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 based on risk and confidence.
• Build the evaluation harnesses that determine when a sub-agent is good enough to ship and that catch regressions in production through continuous monitoring.
• Define metrics and experimentation strategy for sub-agent rollouts; measure real customer impact, not just offline accuracy or eval scores, using robust causal methods.
• Partner with Product, Engineering, and Applied AI teams from problem framing through production deployment, ensuring alignment on assumptions and success criteria.
• Drive a data and modeling culture within Product and Engineering, and mentor other data scientists on the team to elevate standards and practices.
• Research and learn new technologies, tools, and modeling techniques to address evolving business problems and keep solutions at the frontier of applied AI.
• Translate complex modeling and sub-agent behavior into clear recommendations for partners, balancing technical depth with actionable business guidance.
• Own the end-to-end lifecycle of model development, from data exploration and feature engineering through deployment, monitoring, and iterative improvement.
• Apply strong judgment to prioritize initiatives, manage trade-offs between accuracy, latency, cost, and operational risk in production systems.
• Ensure models are robust, explainable, and aligned with Smartsheet's product and compliance requirements across a global customer base.
• Leverage petabyte-scale execution data spanning two decades to uncover insights that drive strategic product decisions and sub-agent behavior.
• Establish best practices for evaluation, monitoring, and governance of AI sub-agents to support scalable and reliable adoption.

Requirements

• 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, including 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, including uplift modeling, difference-in-differences, propensity scoring, and synthetic control.
• Solid foundation in statistics and experimental design, covering hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods.
• Hands-on experience taking LLM- and agent-based systems to production, including tool use, retrieval, multi-step reasoning, evaluation, and guardrails.
• Experience operating ML in production, including 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, with comfort using ML/LLM tooling at scale such as Spark, Databricks, Snowflake, or equivalents, ML frameworks like PyTorch and scikit-learn, and XGBoost/LightGBM, plus visualization tools such as Tableau or similar.
• Experience modeling the customer lifecycle, including churn, expansion, adoption, plan health, lead/account scoring, and fluency in SaaS metrics that drive it such as NRR, GRR, ARR, and cohort economics.
• A pragmatic production bar, focusing on latency, cost, monitoring, drift, hallucination, and what happens when the model or sub-agent is wrong in live systems.
• 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 modeling techniques to address evolving business problems and incorporate state-of-the-art advances into solutions.

Practical notes

• This is a full-time position that 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.
• Hours, travel, visa, or deadlines are not specified in the source; no additional constraints are listed beyond standard full-time employment in a registered US location.