Finance Expert Lead, Gemini Data, DeepMind

Google; Greater London; On-site; GBP 110,000 - 112,000; Full time

Job summary

Google DeepMind is expanding Gemini’s frontier AI capabilities for specialized finance workflows. We are seeking a Finance Expert Lead to map complex Finance processes, identify critical areas for model improvement, and curate high-quality data to make Gemini the ultimate AI assistant for finance professionals.

In this role you will collaborate across teams to drive data-driven breakthroughs while navigating the privacy and ethics standards Google upholds.

Qualifications

• Master's degree in Finance, Economics, Business Administration, or related quantitative field (or equivalent practical experience).
• Experience measuring performance of LLMs or generative AI in financial use cases.

Responsibilities

• Set the Finance data strategy by mapping workflows and prioritizing data coverage and sourcing pipelines.
• Create high-quality Finance data with tasks and rubrics and QA for datasets from vendors, customers, and synthetic pipelines.
• Reproduce model failures and stress-test checkpoints to identify missing domain knowledge and incorrect assumptions.
• Build Finance evaluations by comparing industry benchmarks to measure data strategy success.
• Scale expertise by recruiting SMEs and converting expert contributions into training data.

Skills

Master's degree in Finance

Experience evaluating LLMs for finance

Education

Master's degree in Finance or related field

Job description

Minimum qualifications:

• 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.

Preferred qualifications:

• 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.

About the job

At Google DeepMind, we are expanding Gemini’s frontier AI capabilities into highly specialized professional domains. We are seeking a Finance Expert Lead to shape our foundational data strategy for the financial sector. In this role, you will apply your deep domain knowledge to map complex Finance workflows, identify critical areas for model enhancement, and curate the high-quality data necessary to make Gemini the ultimate AI assistant for Finance professionals.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

France: €130000 - €133000 (EUR) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• 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.

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