Data Scientist

DATA SCIENTIST About Totango We’re building the future of post-sales. As one of the creators of the Customer Success space, we’ve seen firsthand just how much the traditional customer motions of the past are being disrupted; and we have the bravery and conviction to shed our successful past and ride the wave of this new, AI-centric future of post-sales. We’re now embedding automation at the very core of how our platform works: intelligently orchestrating the actions, plays, and decisions that customer-facing teams make every day. We don’t just surface data; we help revenue teams act on it faster, smarter, and with less friction. If you’ve ever thought, “There has to be a better way to run CS and renewals,” this is it. Our culture isn’t for everyone, but for the right person, it’s extremely refreshing. Our shared values: Dissent is constructive. Always assume positive intent. Have a supreme bias for action. Don’t be afraid to burn all the boats. Iterate the pain away. Language is the roadmap of culture. If this resonates, please read on. About the role 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. About you 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, You bring: 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 Bonus-Points if You’ve managed the full ML lifecycle in production: training, deployment, inference, and retraining pipelines You have experience with containerization and model hosting (Docker, AWS) You’ve worked with natural language processing or text/sentiment analysis (e.g., Voice of Customer programs) You have experience with data warehouses like BigQuery or Snowflake You have TypeScript or front-end exposure that helps you collaborate with product and engineering You’ve worked alongside data engineering teams on the client side and can hold your own in a conversation about data pipelines and warehousing What you'll own 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