Finance Data Quality Analyst & Automation (Hybrid)

Mistral; France; Hybrid; EUR 45,000 - 60,000; Full time

Benefits offered by this job

Healthcare coverage

Relocation support

Meal and transportation allowances

Job summary

Mistral is seeking highly motivated Data Quality Analysts with strong analytical skills to join our Human Data Annotation team within the Science organisation. This is a hybrid quality reviewing and tooling role: auditing Finance annotations and improving data quality for AI models.

You’ll collaborate with annotators, program managers, and science stakeholders to refine guidelines and build internal tools, dashboards, and automation to streamline reviews and edge-case handling.

Qualifications

• Masters in Finance (or equivalent)
• 3+ years professional experience in the financial domain
• Excellent analytical and research capabilities
• Ability to read JSONL files and basic output formats
• Strong judgement with complex instructions and limited data
• Excellent communication and organizational skills
• Adaptable to changing requirements and repetitive tasks
• Professional English writing and comprehension skills

Responsibilities

• Review and audit finance annotations to meet rubrics for AI training/evaluation
• Generate and validate high-quality finance-oriented data annotations when needed
• Build and maintain internal tools and automation to streamline annotation workflows
• Troubleshoot tooling and guideline issues for annotators
• Organise annotator training and coordinate feedback between annotators and reviewers

Skills

Finance data domain

Data quality

Analytical skills

English proficiency

Education

Masters in Finance

Tools

Python

Unix

Git

Job description

Mistral is seeking highly motivated Data Quality Analysts with strong analytical skills to join our Human Data Annotation team within the Science organisation. This is a hybrid quality reviewing and tooling role: auditing Finance annotations and improving data quality for AI models.

You’ll collaborate with annotators, program managers, and science stakeholders to refine guidelines and build internal tools, dashboards, and automation to streamline reviews and edge-case handling.