RippleMatch
HR Tech • B2B • B2C
RippleMatch is an AI-driven job matching platform designed to connect candidates with top internships and entry-level job opportunities tailored to their skills and goals. By streamlining the job search process, RippleMatch helps candidates receive faster responses from employers, increasing their chances of landing interviews significantly compared to traditional job boards. The platform focuses on providing personalized matches and guidance to ensure candidates find roles that align with their interests and qualifications.
Data Scientist
Job not on LinkedIn
November 24
RippleMatch
HR Tech • B2B • B2C
RippleMatch is an AI-driven job matching platform designed to connect candidates with top internships and entry-level job opportunities tailored to their skills and goals. By streamlining the job search process, RippleMatch helps candidates receive faster responses from employers, increasing their chances of landing interviews significantly compared to traditional job boards. The platform focuses on providing personalized matches and guidance to ensure candidates find roles that align with their interests and qualifications.
📋 Description
• 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
🎯 Requirements
• 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 • 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
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