Data Scientist at Totango

Compensation

$95,000 – $115,000/yr

Location: New York, New York
Type: Full-time
Posted: Apr 24, 2026

Description

Totango is seeking a Data Scientist responsible for managing the entire lifecycle of custom machine learning models. This involves building, tuning, deploying, and continuously improving predictive models used by customers to drive decisions regarding churn, customer health, and growth. The role requires translating business needs into analytical frameworks, communicating results effectively to non-technical stakeholders, and collaborating with various internal teams.

Required Skills

Supervised Machine Learning Models
Regression and Tree-based Classification Methods
Python Skills
SQL
Statistical Analysis
Hypothesis Testing
Causal Inference
Time Series Methods
SHAP/Shapley Values

Additional Information

Required Previous Experiences: 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.
Preferred Qualifications: 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.