2025 Compensation Changes & Prioritization: AI vs. Data Science Roles

AI and Data Science may share tools, but their missions—and market value—are diverging. AI is shifting from experimental to operational, and the talent market is adapting accordingly. Compensation is also shifting to reflect where the scarcest, most business-critical skills now reside.

‍Data Scientists are decision enablers. They specialize in:

• Statistical modeling and classical machine learning
• Experiment design and causal inference
• Forecasting, segmentation, and predictive analytics
• Building models embedded into BI tools or deployed via batch and stream scoring

Driving insightful business decisions, often powering executive dashboards and operational efficiencies.

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‍AI Professionals, on the other hand, are system builders. They focus on:

• Designing and deploying ML/LLM systems that interact with users and real-world workflows
• Working across structured, semi-structured, and multimodal data sources
• Fine-tuning, distilling, and grounding large language models (LLMs)
• Running RLHF (Reinforcement Learning with Human Feedback) and safety evaluations
• Orchestrating MLOps, toolchains, and governance protocols to ensure scale and compliance

Powering intelligent automation, AI products, and business-critical applications.

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METHODOLOGY

The Burtch Works 2025 AI & Data Science Compensation Report analyzed a sample of 866 professionals across a 12-month cycle:

• 724 Data Scientists
• 162 AI Professionals

All respondents were segmented using the same rigorous criteria we’ve applied in previous years—ensuring comparability, credibility, and continuity for decision-makers.