Finance Data Annotation Quality Analyst

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.

• 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.

• 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

• 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)