Asks for

The ad says“Strong grasp of causal inference for intervention design and lifecycle modeling: uplift modeling, difference-in-differences, propensity scoring, and synthetic control”
The ad says“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”
The ad says“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”
The ad says“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)”
The ad says“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”
The ad says“Strong grasp of causal inference for intervention design and lifecycle modeling: uplift modeling, difference-in-differences, propensity scoring, and synthetic control”
The ad says“Solid foundation in statistics and experimental design: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods”
The ad says“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”
The ad says“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”
The ad says“Solid foundation in statistics and experimental design: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods”
The ad says“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)”
The ad says“Hands-on experience taking LLM- and agent-based systems to production: tool use, retrieval, multi-step reasoning, evaluation, and guardrails”
The ad says“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”
The ad says“Solid foundation in statistics and experimental design: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods”
The ad says“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”
The ad says“Solid foundation in statistics and experimental design: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods”
The ad says“Strong grasp of causal inference for intervention design and lifecycle modeling: uplift modeling, difference-in-differences, propensity scoring, and synthetic control”
The ad says“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)”
The ad says“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)”
The ad says“Solid foundation in statistics and experimental design: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods”
The ad says“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”
The ad says“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)”
The ad says“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)”
The ad says“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)”
The ad says“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)”
The ad says“Solid foundation in statistics and experimental design: hypothesis testing, power analysis, multiple comparisons, sequential testing, and quasi-experimental methods”
The ad says“Strong grasp of causal inference for intervention design and lifecycle modeling: uplift modeling, difference-in-differences, propensity scoring, and synthetic control”
The ad says“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)”
The ad says“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”
The ad says“Strong grasp of causal inference for intervention design and lifecycle modeling: uplift modeling, difference-in-differences, propensity scoring, and synthetic control”
The ad says“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)”

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

Current US Perks & Benefits:

Employer subsidized medical/vision and dental coverage for full-time employees
401k Match to help you save for your future (50% of your contribution up to the first 6% of your eligible pay)
Monthly stipend to support your work and productivity
Flexible Time Away Program, plus Sick Time Off
US employees are automatically covered under Smartsheet-sponsored life insurance, short-term, and long-term disability plans
US employees receive 12 paid holidays per year
Up to 24 weeks of Parental Leave
Personal paid Volunteer Day to support our community
Opportunities for professional growth and development including access to Udemy online courses
Company Funded Perks, including a counseling membership, local retail discounts, and your own personal Smartsheet account
Teleworking options from any registered location in the U.S. (role specific)

Smartsheet provides a competitive base salary range for roles that may be hired in different geographic areas we are licensed to operate our business from. Actual compensation is determined by several factors including, but not limited to, level of professional, educational experience, skills, and specific candidate location. In addition, this role will be eligible for a market competitive incentive opportunity.

US Base Salary Pay Range

$155,000—$185,000 USD

Get to Know Us:

At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together.

Equal Opportunity Employer:

Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information.

If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.

#LI-Remote