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# Finance Expert Lead, Gemini Data, DeepMind

- **Company:** Google
- **Location:** Paris, France
- **Experience:** Expert
- **Salary:** €130,000.0 - €133,000.0
- **Contract:** Permanent contract
- **Skills:** Machine Learning, Large Language Models, Data Strategy, Google Gemini, DeepMind
- **Published:** October 13, 2026
- **Apply:** https://dejobs.org/x/x/2D061C71C3714C05854C190A215768D8/job/

## About the Role

* Master's degree in Finance, Economics, Business Administration, a related quantitative field, or equivalent practical experience.
 * Experience measuring the performance of Large Language Models (LLMs) or generative AI tools and optimizing them for financial use cases., * Demonstrated dual expertise bridging both fields, such as transitioning from a direct financial professional into a technical role or vice versa.
 * Experience breaking down complex professional workflows into concrete tasks, evaluation rubrics, and quality standards that can be used to train and evaluate models.
 * Experience prioritizing capabilities and developing a strategic point of view on how advanced AI models can unlock value for financial professionals.
 * Excellent cross-functional communication skills, with experience collaborating closely with technical partners.
 * Strong technical and research capabilities, including proficiency in programming evaluating machine learning models, and applying Large Language Models to complex financial workflows.

## Description

* Set the Finance data strategy by mapping workflows and tools into a use-case taxonomy, and partnering with research to prioritize capabilities, data coverage, and sourcing pipelines.
 * Create high-quality Finance data by designing tasks and rubrics, and quality assurance for datasets from vendors, customers, and synthetic pipelines for correctness, realism, and coverage to close gaps.
 * Steer data priorities using model failures by reproducing reported losses and stress-testing checkpoints on real workflows to identify missing domain knowledge, broken conventions, and false assumptions.
 * Build Finance evaluations by assessing industry benchmarks to measure data strategy success, and advise cross-team evaluation efforts on domain coverage and realism.
 * Scale expertise by recruiting and guiding subject matter experts (SMEs), acting as the domain authority to enforce vendor quality, and converting Googler expert contributions into usable training and evaluation data.