Finance AI Lead: Shape Data Strategy for Finance

DeepMind Technologies Limited; New York (NY); On-site; USD 207,000 - 300,000; Full time

Job summary

Google DeepMind is seeking a Finance Expert Lead to shape our foundational data strategy for the financial sector. You will map complex Finance workflows, identify model improvement areas, and curate high-quality data to empower Gemini as an AI assistant for finance professionals.

This role emphasizes collaboration with research teams, vendors, and SMEs to ensure data coverage, realism, and applicability to real-world financial tasks.

Qualifications

• Master's degree or equivalent practical experience in finance or related field.
• Experience measuring LLMs for financial use cases.
• Excellent cross-functional communication with technical partners.
• Strong technical and research capabilities applying LLMs to financial workflows.

Responsibilities

• Set the Finance data strategy by mapping workflows and tools into a use-case taxonomy and prioritizing capabilities, data coverage, and sourcing pipelines.
• Create high-quality Finance data by designing tasks and rubrics, and QA for datasets from vendors, customers, and synthetic pipelines.
• Steer data priorities by reproducing reported losses and stress-testing checkpoints on real workflows.
• Build Finance evaluations by assessing industry benchmarks to measure data strategy success.
• Scale expertise by recruiting SMEs and converting expert contributions into training data.

Skills

LLM performance measurement

Finance data strategy

Cross-functional leadership

ML model evaluation

Programming for ML

Education

Master's degree in Finance or related field

Job description

Google DeepMind is seeking a Finance Expert Lead to shape our foundational data strategy for the financial sector. You will map complex Finance workflows, identify model improvement areas, and curate high-quality data to empower Gemini as an AI assistant for finance professionals.

This role emphasizes collaboration with research teams, vendors, and SMEs to ensure data coverage, realism, and applicability to real-world financial tasks.