Senior Data Scientist

Divisions Maintenance Group•Cincinnati, OH

23d•Onsite

About The Position

DMG operates a tech-enabled facilities maintenance marketplace serving a large and growing customer base across multiple segments. The Revenue Strategy & Planning team has built a strong data infrastructure, and now we need someone to turn that data into pricing intelligence. The Data Scientist III sits on the Business Intelligence team, which is dedicated to strategic, long-cycle analytical work rather than day-to-day deal execution. Your models will be operationalized by the Pricing Engineer and used by the Sales Ops execution team to win deals. This role is designed for an experienced applied data scientist who can build models that go into production and directly influence deal outcomes, not someone looking for pure research or reporting.

Requirements

Bachelor’s degree in an analytical field such as Data Science, Computer Science, Applied Mathematics, Operations Research or Economics.
7+ years building pricing systems, CPQ platforms, or revenue operations tooling in a production environment.
Advanced SQL skills with experience in Snowflake or a comparable cloud data platform.
Python mastery for automation, data pipelines, and model operationalization.
Experience translating analytical models into production systems that non-technical users can operate.
Ownership-oriented mindset with attention to detail and a focus on reliability.
Ability to manage the stress of a fast-paced environment.
Ability to meet the in-person requirements of the team and/or business needs.

Nice To Haves

PhD is preferred but not required.
Understanding of facilities maintenance, services marketplaces, or multi-trade pricing is a strong plus.
Comfort using AI tools to speed up coding, problem-solving, and documentation.

Responsibilities

Pricing Model Development - Build and iterate on pricing models that determine how we bid across trades, geographies, and customer segments. Models need to account for provider economics, competitive positioning, and margin targets.
Win/Loss Analytics - Own the analysis of deal outcomes. Identify pricing sensitivity by trade, customer segment, and competitive set. Build an analytical foundation that tells us why we win and why we lose.
Margin Prediction & Accuracy - Develop and maintain margin prediction models that compare quoted margins to realized margins on completed work. Find systematic prediction errors and correct them.
Model Validation - Track how pricing models perform against deals priced outside of the standard model. Use this data to measure model effectiveness and identify areas for improvement.
Total Cost of Ownership & Value Modeling - Support the development of Total Cost of Ownership frameworks that help the sales organization compete on value instead of rate. Quantify the cost advantages our marketplace model provides to customers.
Revenue Analytics - Use structured revenue decomposition methods to identify where growth is coming from and where execution is breaking down at the account level.
Other duties as assigned by management.