Senior Data Scientist II

Smartsheet

Senior Data Scientist II at Smartsheet developing AI models and sub-agents for growth and retention in the customer lifecycle. Collaborating with Product and Engineering teams to implement solutions.

Posted 7/11/2026full-timeRemote • Washington • 🇺🇸 United StatesSenior💰 $155,000 - $185,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep applied machine learning expertise, including traditional and deep learning techniques, and a solid foundation in statistics and experimental design. Proficient in SQL and Python, with hands-on experience in deploying LLM and agent-based systems in production while effectively communicating insights to cross-functional teams.

Highest-signal resume keywords

Deep Applied ML ExpertiseCausal Inference for Intervention DesignHands-On Experience with LLM SystemsProficient in SQL and PythonExperience Modeling Customer Lifecycle

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Gradient BoostingRegularized Linear ModelsTransformer-Based Sequence ModelsCausal MLUplift ModelingHypothesis TestingFeature EngineeringModel MonitoringDrift DetectionExperimental Design

Soft Skills

Effective Cross-Functional PartnershipsClear Communication to Technical AudiencesAbility to Thrive in Dynamic Environments

Tools & Technologies

SparkDatabricksSnowflakePyTorchScikit-LearnXGBoostLightGBMTableau

Industry Keywords

Customer LifecycleSaaS MetricsChurn RiskAccount Health ScoringGrowth TrajectoriesPrivacy-Aware DesignEvaluation Harnesses

Tech Stack

Tools & technologies

PythonPyTorchScikit-LearnSparkSQLTableau

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

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)