Sr. Data Scientist II (remote Eligible)

Smartsheet Smartsheet · Seattle · United States · Business Intelligence & Ops

Smartsheet is seeking a Sr. Data Scientist II to build ML models and AI sub-agents for customer lifecycle management. The role involves end-to-end development, from problem framing to production deployment, focusing on predictive models, agent design, and data foundations. The position emphasizes shipping AI systems into production for millions of users, leveraging petabyte-scale data.

What you'd actually do

  1. 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
  2. Build the predictive and prescriptive models that power those sub-agents churn risk, growth, adoption trajectories, account health scoring, and similar lifecycle problems
  3. Develop the data foundations and knowledge layer those sub-agents reason over, applying responsible aggregation and privacy-aware design
  4. Design the tools, retrieval, and grounding strategies each sub-agent uses; decide when a sub-agent should act, recommend, defer, or escalate
  5. Build the evaluation harnesses that determine when a sub-agent is good enough to ship and that catch regressions in production

Skills

Required

  • 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

Nice to have

  • advanced degree in a quantitative field (Statistics, CS, ML, Economics, Operations Research, or similar) preferred

What the JD emphasized

  • Hands-on experience taking LLM- and agent-based systems to production
  • A pragmatic production bar: latency, cost, monitoring, drift, hallucination, and what happens when the model or sub-agent is wrong

Other signals

  • AI agents
  • LLM reasoning
  • predictive models
  • production deployment
  • customer lifecycle