Finance Data Annotation Quality Analyst

About Mistral Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.

We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

Role Summary We’re seeking highly motivated Data Quality Analysts with strong analytical skills and a keen eye for detail to join our Human Data Annotation team within the Science organisation. This is a hybrid quality reviewing and tooling role:

• you'll spend the majority of your time reviewing and auditing Finance (any subdomain) annotations against rubrics to ensure data used for training and evaluating AI models meets the highest standards of quality and efficiency.
• and the remainder building, maintaining, and troubleshooting annotation operations.

You'll collaborate closely with the annotators, technical program manager, and science stakeholders, and contribute to refining the guidelines and processes that shape how our data is produced.

Key Responsibilities

• In collaboration with the technical team, review/audit annotations, clarify requirements, share insights, surface systemic issues, edge cases, and gaps in guidelines, to improve annotation processes and guidelines.
• Generate and validate high-quality finance-oriented data annotations yourself when needed, based on guidelines and continuous feedback, for the development and evaluation of AI models
• Build and maintain internal tools and automation that streamline annotation or review workflows such as visualization dashboards, batch configuration scripts, output management utilities, and similar
• Troubleshoot campaign issues (e.g. tooling, guidelines) for annotators
• Organise annotator training and coordinate feedback between annotators and reviewers.

About You

• A degree (minimum Masters level) in a Finance discipline.
• At least 3 years of professional experience in the Financial domain.
• Outstanding analytical and research skills
• Some familiarity with programming languages, with the ability to read standard output formats such as .jsonl files.
• Exercises great judgement with complex instructions, limited data, and/or multiple information sources and ability to surface edge cases, ambiguities, or gaps in guidelines accordingly.
• Excellent communication, interpersonal, and organisational abilities
• Adapts easily to dynamic environments and changing requirements
• Appetite for operational work and high tolerance for repetitive tasks with a sustained focus and accuracy on detail-oriented, high-volume review work.
• Professional proficiency in English, with strong writing and comprehension skills

Nice to have

• Familiarity with rubric-based evaluation concepts, inter-annotator agreement, or quality measurement for human-labeled data
• Experience building an annotation team through interviews and training
• Proficient in at least one programming language (e.g. Python)
• Prior experience in data annotation for AI/ML, especially LLM training (SFT, RLHF, preference data), evals/benchmarks, or agentic data
• Comfortable working in a Unix-like terminal and using git workflows (branches, merges, resolving conflicts)

What we offer We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.

For the most up-to-date details on benefits available in your location, please refer to our Benefits page.

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