Data Scientist

Totango • Toronto, ON

7d

About The Position

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.

Requirements

• 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

Nice To Haves

• Managed the full ML lifecycle in production: training, deployment, inference, and retraining pipelines
• Experience with containerization and model hosting (Docker, AWS)
• Worked with natural language processing or text/sentiment analysis (e.g., Voice of Customer programs)
• Experience with data warehouses like BigQuery or Snowflake
• TypeScript or front-end exposure that helps you collaborate with product and engineering
• Worked alongside data engineering teams on the client side and can hold your own in a conversation about data pipelines and warehousing

Responsibilities

• 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

Benefits

• Equal opportunity employer
• No discrimination based on race, religion, national origin, gender identity, age, sexual orientation, or any other protected class
• Commitment to creating an inclusive environment for all employees

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 11-50 employees