Finance Expert Lead, Gemini Data

On-site in London·Added 4 days ago (last checked 15 Oct)

Google

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Requirements

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.

The role

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
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: London, UK; Paris, France; New York, NY, USA.

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.

Hiring process

4 steps3 interviewsa task to do6–8 weeks in all

Apply online
Assessments
Online test · Not every role
You may be asked to take the Google Hiring Assessment (workstyle skills) and, for some roles, role-related assessments such as a coding exercise.
Recruiter conversations
With a recruiter · Phone call · 1–2 sessions
One or two shorter conversations assessing key skills, sharing information about the organisation and team, and asking about your preferences such as location.
Project work
In your own time · Not every role
For some roles, a small project before interviews: a case study, writing or code samples.
Interviews
With a rotating cast of Googlers
A panel of structured interviews, over video or in person, with a rotating cast of Googlers; open-ended role-related questions scored against the same rubric for every candidate, no brainteasers.
Decision and offer · With a recruiter

Structured interviews: every candidate for a role is scored against the same rubric; no brainteasers.
AI tools are not permitted during interviews.
For some roles, pre-selected interviews happen virtually before final interviews onsite.