Senior Data Scientist (Remote | Part-Time | $100 -$120/hr)

Senior Data Scientist (Remote | Part-Time | $100-$120/hr)

Mercor is partnering with a leading AI research lab to hire experienced Data Scientists specializing in AI task evaluation and statistical analysis.

In this role, you will conduct comprehensive failure analysis on AI agent performance across finance-sector tasks - identifying systemic patterns, diagnosing performance bottlenecks, and improving model evaluation frameworks.

You'll work closely with AI engineers and research analysts to transform raw evaluation data into actionable insights, strengthening the quality, fairness, and reliability of large-scale AI systems., * Statistical Failure Analysis: Identify recurring patterns in AI agent failures across task components (prompts, rubrics, file types, tags, etc.).

• Root Cause Analysis: Determine whether issues stem from task design, rubric clarity, file complexity, or agent limitations.
• Dimensional Analysis: Examine performance variations across finance sub-domains, file structures, and evaluation criteria.
• Visualization & Reporting: Build dashboards and analytical reports that highlight edge cases, performance clusters, and opportunities for improvement.
• Framework Enhancement: Recommend refinements to rubric design, evaluation metrics, and task structures based on empirical findings.
• Stakeholder Communication: Present key insights to data labeling teams, ML engineers, and research collaborators.

• Strong foundation in statistical analysis, hypothesis testing, and pattern recognition.
• Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis.
• Hands-on experience with exploratory data analysis (EDA) and feature interpretation.
• Understanding of AI/ML evaluation methodologies and LLM performance metrics.
• Skilled in using Excel, SQL, and data visualization tools (e.g., Tableau, Looker)., * Experience with AI/ML model evaluation or quality assurance pipelines.
• Background in finance or interest in learning financial domain structures.
• Familiarity with benchmark datasets, failure mode analysis, and evaluation frameworks.
• 2-4 years of relevant professional experience in data science, analytics, or applied statistics.