Sr. Data Scientist II (Remote Eligible)

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day.

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