Senior Data Scientist - US

Company: mSupply

Location: Remote (Remote)

Type: Full-time

Remote: Yes

Posted: 2026-03-30

About this role

About mSupply. mSupply is North America’s leading distributor of OEM repair parts and equipment, serving professionals in the appliance, HVAC, plumbing, commercial kitchen and pool/spa industries. Headquartered in St. Louis, MO, mSupply is a multi-billion dollar enterprise offering an extensive product range, industry expertise and seamless service. With more than 2,000 associates across the U.S. and Canada, mSupply’s family of brands delivers with speed, reliability and precision through its branches, distribution centers and extensive fleet of delivery vehicles. Shipped orders reach 93% of U.S. customers via next-day ground delivery and 100% within two days. For more information, visit mSupply.com.

The Senior Data Scientist is responsible for owning, maintaining, and evolving machine-learning pricing algorithms that set and optimize prices across products, customers, and channels to drive profitable growth in a B2B environment. This role will partner with cross-functional teams to translate complex findings into clear, actionable recommendations for pricing, sales, category management, and finance.

## Duties & Responsibilities

## ML Model Coding, Maintenance, Continuous Improvement

  • Own pricing ML models end-to-end: design, build, validate, deploy, and monitor algorithms (elasticity estimation, segmentation, discount optimization, list/target/contract price recommendations).
  • Productionize & maintain pipelines for data prep, feature engineering, training, inference, and monitoring (drift, stability, explainability, governance).
  • Run experiments: design A/B and quasi-experimental tests; define KPIs and guardrails; analyze impact on revenue, margin, and win rate.
  • Data stewardship: ensure data quality, privacy, and responsible AI practices.
  • Uplift analytics: build dashboards for model health and price realization; document methods and assumptions; champion best practices in code quality and reproducible research.
  • Scale MLOps: implement CI...

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