Data Scientist, Totango

• You’re a builder and a communicator. You’re equally comfortable writing a regression pipeline and walking a VP of Customer Success through what the outputs mean. You don’t think you have it all figured out; you’re hungry, flexible, and excited to adapt and grow,
• Hands-on experience building and deploying supervised machine learning models, specifically regression and tree-based classification methods (gradient boosting, random forests, etc.)
• Strong Python skills; SQL is a must for working directly with large datasets and data warehouses
• A solid foundation in statistical analysis. Hypothesis testing, causal inference, and time series methods
• Experience interpreting and explaining model outputs (e.g., SHAP / Shapley values) to non-technical stakeholders
• The ability to take a complex model or analytical finding and break it down into something a business audience can understand and act on
• Comfort working cross-functionally with CS, product, and data engineering teams
• A genuine bias for action; you don’t wait to be told what to analyze next

Desirable

• The Data Scientist owns the full lifecycle of custom machine learning models that power how Totango’s customers understand and act on their data
• This isn’t a dashboard job or a reporting role. It’s deep, hands-on modeling work: building, tuning, deploying, and iterating on predictive models that real customers use to make real decisions about churn, health, and growth
• You’ll partner directly with customer-facing teams and client stakeholders to translate messy business questions into rigorous analytical frameworks, then communicate findings in ways that non-technical audiences can act on. You’re the bridge between the math and the mission
• Custom ML models end-to-end: scoping, training, calibration, and ongoing maintenance
• Exploratory and secondary analysis that generates population-level insights customers use to run their operations
• Model interpretability: Ensuring that every prediction comes with an explanation a customer can act on
• Statistical analysis in service of client hypotheses: running tests, validating hunches, and reporting findings with clarity
• Collaboration with customer-facing teams and client stakeholders to surface insights and translate them into action
• Slide decks and written reports that communicate complex findings to non-technical audiences
• Staying close to production: monitoring deployed models and triggering retraining as needed