Data Scientist

Mercor; Frankfurt; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A leading analytics firm in Frankfurt, Germany, is seeking an AI Task Evaluation & Statistical Analysis Specialist. This role focuses on conducting statistical failure analysis on AI agent performance across various finance-sector tasks. Key responsibilities include identifying patterns in failures, performing root cause analysis, and creating visual reports to present insights. The ideal candidate will have strong statistical expertise, proficiency in Python or R, and experience in data analysis. This position offers an exciting opportunity to improve evaluation frameworks in a dynamic environment.

Qualifikationen

• Strong foundation in statistical analysis and pattern recognition.
• Proficiency in Python or R for data analysis.
• Experience with exploratory data analysis and data insights.

Aufgaben

• Identify patterns in AI agent failures across tasks.
• Determine root causes of failures in task design or agent limitations.
• Create dashboards highlighting failure clusters and improvement opportunities.

Kenntnisse

Statistical analysis

Hypothesis testing

Pattern recognition

Python (pandas, scipy, matplotlib/seaborn)

Data visualization

SQL

Tools

Excel

Tableau

Looker

Jobbeschreibung

AI Task Evaluation & Statistical Analysis Specialist

We’re seeking a data‑driven analyst to conduct comprehensive failure analysis on AI agent performance across finance‑sector tasks. You’ll identify patterns, root causes, and systemic issues in our evaluation framework by analyzing task performance across multiple dimensions (task types, file types, criteria, etc.).

Role Overview

We’re seeking a data‑driven analyst to conduct comprehensive failure analysis on AI agent performance across finance‑sector tasks. You’ll identify patterns, root causes, and systemic issues in our evaluation framework by analyzing task performance across multiple dimensions (task types, file types, criteria, etc.).

Key Responsibilities

• Statistical Failure Analysis: Identify patterns in AI agent failures across task components (prompts, rubrics, templates, file types, tags)

• Root Cause Analysis: Determine whether failures stem from task design, rubric clarity, file complexity, or agent limitations

• Dimension Analysis: Analyze performance variations across finance sub‑domains, file types, and task categories

• Reporting & Visualization: Create dashboards and reports highlighting failure clusters, edge cases, and improvement opportunities

• Quality Framework: Recommend improvements to task design, rubric structure, and evaluation criteria based on statistical findings

• Stakeholder Communication: Present insights to data labeling experts and technical teams

Required Qualifications

• Statistical Expertise: Strong foundation in statistical analysis, hypothesis testing, and pattern recognition

• Programming: Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis

• Data Analysis: Experience with exploratory data analysis and creating actionable insights from complex datasets

• AI/ML Familiarity: Understanding of LLM evaluation methods and quality metrics

• Tools: Comfortable working with Excel, data visualization tools (Tableau/Looker), and SQL

Preferred Qualifications

• Experience with AI/ML model evaluation or quality assurance
• Background in finance or willingness to learn finance domain concepts
• Experience with multi‑dimensional failure analysis
• Familiarity with benchmark datasets and evaluation frameworks
• 2-4 years of relevant experience