Data Science Evaluation Architect

Mercor; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

Mercor is partnering with a leading AI research organization to engage experienced data scientists for a project focused on evaluating how well AI systems perform real-world data science work.

Rather than producing deliverables yourself, you will define what excellent work looks like: designing task-specific grading criteria and scoring completed work samples with rigorous, well-reasoned written justifications.

Qualifications

• 5+ years of professional data science experience in industry.
• Background in business operations, product, or growth data science at top-tier technology companies.
• Deep fluency in experiment design and A/B testing, metric definition, SQL/Python analysis, and communicating findings to executive stakeholders.
• Exceptionally strong written communication.
• Detail-oriented, consistent, and comfortable having your judgment reviewed and calibrated against peers.
• Prior experience with AI training, evaluation, or human-data projects is a strong plus.

Responsibilities

• Design precise, task-specific grading criteria for real-world data science deliverables (analyses, models, dashboards, experiment readouts, and written recommendations).
• Score AI-generated and human work samples against those criteria, with detailed written justifications for every score.
• Apply consistent, evidence-based judgment so that scores are reproducible and defensible.
• Incorporate structured feedback from senior reviewers and iterate quickly on your work.

Skills

Data science

Experiment design

A/B testing

SQL

Python

Written communication

Tools

SQL

Python

Jupyter

Job description

Mercor is partnering with a leading AI research organization to engage experienced data scientists for a project focused on evaluating how well AI systems perform real-world data science work.

Rather than producing deliverables yourself, you will define what excellent work looks like: designing task-specific grading criteria and scoring completed work samples with rigorous, well-reasoned written justifications.