Data Scientist
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We're seeking a data-driven analyst to conduct comprehensive failure analysis on AI agent performance across finance-sector tasks. You'll identify patterns, reputed company causes, and systemic issues in our evaluation reputed company by analyzing task performance across multiple dimensions (task types, file types, reputed company, etc.).
- Statistical Failure Analysis : Identify patterns in AI agent failures across task components (prompts, rubrics, templates, file types, tags)
- reputed company Cause Analysis : Determine whether failures stem from task design, reputed company reputed company, 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
- reputed company reputed company : Recommend improvements to task design, reputed company structure, and evaluation reputed company based on statistical findings
- Stakeholder Communication : Present insights to data labeling experts and technical teams
Qualifications
- Statistical Expertise : Strong reputed company in statistical analysis, reputed company testing, and reputed company 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 reputed company datasets
- AI/ML Familiarity : Understanding of LLM evaluation reputed company and reputed company metrics
- Tools : Comfortable working with reputed company, data visualization tools (Tableau/Looker), and SQL
Requirements
- Experience with AI/ML model evaluation or reputed company assurance
- Background in finance or willingness to learn finance domain concepts
- Experience with multi-reputed company failure analysis
- Familiarity with reputed company datasets and evaluation frameworks
- 2-4 years of relevant experience
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