Jobs at Dave Inc.

Own and optimize Dave's payments and network partnerships (card networks, processors, tokenization, fraud, reconciliation, ATMs). Drive commercial strategy, cost management, contract renewals, and cross-functional initiatives with Product, Engineering, Finance, Risk, Legal, and Ops to improve reliability, economics, and scalability of payments infrastructure.

Lead and scale Dave's data and risk partnerships across fraud, identity, open banking, bank connectivity, and credit bureaus. Own partner sourcing, diligence, negotiation, contracting, implementation, governance, and performance measurement to improve underwriting, fraud controls, member eligibility, and repayment outcomes.

Lead strategy and execution for distribution and marketing partnerships to drive user acquisition. Source, negotiate, structure, launch, and scale partner deals; collaborate with product, legal, finance, analytics, and marketing to measure and optimize performance.

Owner of product marketing strategy and execution: customer and member segmentation, positioning, value propositions, GTM for new and existing products, competitive intelligence, and establishing scalable PMM processes grounded in research and analytics.

Lead product strategy for MarTech ecosystem, managing user acquisition, cross-sell, and retention across web and app through data-driven insights and experimentation.

The role involves designing reconciliation systems for financial data, improving anomaly detection, automating processes, and collaborating cross-functionally to enhance data quality and reliability in financial operations.

Lead a Front End Infrastructure squad that builds and maintains mobile platform systems (CI/CD, shared libraries, observability, analytics, experimentation, design systems). Balance people management and hands-on technical guidance across React Native, iOS, Android, and developer tooling; drive roadmap, architecture, hiring, cross-team collaboration, and platform health metrics.

The Lead Machine Learning Scientist will develop and implement ML strategies to enhance marketing and product capabilities, identify opportunities for growth using data, lead core model development, and improve marketing spend efficiency through insights and experimentation.

Design and automate compliance systems to enhance efficiency in audit readiness. Collaborate with teams to integrate tools and optimize processes.