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# Product Data Scientist

- **Company:** Maintainx Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Software as a Service, Python (Programming Language), Logistic Regression, Machine Learning, Usage Analysis, Random Forest, Build Management, Scikit Learn, Machine Learning Operations, Databricks
- **Published:** June 24, 2026
- **Apply:** https://www.dice.com/job-detail/4f997875-af3b-407d-a666-2ae192a5222e

## About the Role

* Senior Product Data Scientist or Senior Data Scientist title with 5-7 years of relevant experience
 * Hands-on experience with product experimentation - A/B testing, causal inference, statistical significance
 * Python proficiency including scikit-learn and MLflow
 * Experience building ML models (GBMs, random forest, linear/logistic regression)
 * Background in B2B SaaS or B2C SaaS
 * Strong cross-functional communication - you've worked directly with PMs, engineers, and designers, not just a sales or finance audience
 * Executive presence: you can turn a complex model output into a business recommendation a VP can act on

Nice-to-haves:

 * Experience with Databricks
 * Familiarity with Amplitude or similar event analytics platforms

## Description

We're hiring a Senior Product Data Scientist to join our Product Analytics team. You'll be the first dedicated Data Scientist on a team that has operated as a product analytics function - meaning you'll define what rigorous experimentation and ML-driven insight looks like at MaintainX, and directly influence how our product evolves.

 * Design and lead product experimentation programs (A/B tests, causal inference) that drive measurable improvements in product adoption and retention
 * Build and deploy ML models - GBMs, random forests, logistic regression - that turn product signals into actionable decisions for PMs and engineering leads
 * Partner cross-functionally with Product, Design, and Engineering to frame business problems as data problems and translate findings into decisions
 * Translate model outputs and experimental results into clear business narratives for senior stakeholders and executives
 * Establish best practices for experimentation and ML workflows


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