Senior Data Scientist II
New
S
SmartsheetSaaS / AI
REMOTE, USAFull-TimeSenior
Salary$155,000 — $185,000 USD
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Job Details
- Experience
- 8+ years of experience (or 10+ years of experience)
- Required Skills
- PythonSQLMachine LearningSnowflakeSparkDatabricksDeep Learning
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 in gradient boosting, linear models, transformers, embeddings, causal ML, and contextual bandits.
- Strong grasp of causal inference (uplift modeling, difference-in-differences, propensity scoring, synthetic control).
- Solid foundation in statistics and experimental design (hypothesis testing, power analysis).
- Hands-on experience taking LLM- and agent-based systems to production (tool use, retrieval, reasoning, evaluation, guardrails).
- Experience operating ML in production (pipelines, monitoring, drift detection, retrain cadence, serving trade-offs).
- Proficient in SQL and Python.
- Familiarity with ML/LLM tooling (Spark, Databricks, Snowflake) and ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM).
- Experience modeling the customer lifecycle (churn, expansion, adoption, lead/account scoring) and SaaS metrics.
- Strong track record of cross-functional partnership and communication.
Responsibilities
- Design and ship AI sub-agents that act across the customer lifecycle, combining predictive models, retrieved context, and LLM reasoning.
- Build the predictive and prescriptive models that power those sub-agents (churn risk, growth, adoption trajectories, account health).
- Develop the data foundations and knowledge layer those sub-agents reason over.
- Design the tools, retrieval, and grounding strategies for sub-agents.
- Build evaluation harnesses to determine model quality and catch regressions.
- Define metrics and experimentation strategy for sub-agent rollouts.
- Partner with Product, Engineering, and Applied AI teams from problem framing through production deployment.
- Drive a data and modeling culture and mentor other data scientists.
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