Data Scientist

Send an email to bharathkumar.sampath@halogion.com with the Subject “Data Scientist” showing your interest in the role to get priority consideration.

At Halogion, we are an Independent member of Mercor referral partner program. We refer candidates to our partner that collaborates with world’s leading AI research labs to build and train cutting-edge AI models

We are 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

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.