Intuit Credit Karma

Intuit Credit Karma

Oakland, CA
1,320 Total Employees
1,030 Local Employees
Year Founded: 2007

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Jobs at Intuit Credit Karma

Search the 6 jobs at Intuit Credit Karma

15 Hours Ago
San Francisco
Fintech
Lead the credit risk data science initiatives for the new and evolving CK Money product offerings focusing on the lending domain. Provide technical leadership, contribute to data strategy, research and implement machine learning approaches, and represent the team in internal and external forums.
Fintech
As a Product Manager for Data Sharing & Platform strategy at Credit Karma, you will develop and execute a comprehensive technical product vision, lead product development, collaborate cross-functionally, solve data-driven problems, track performance metrics, ensure compliance, and manage partnerships.
Fintech
Lead the company's total rewards strategy and delivery, encompassing compensation and benefits programs. Partner with executive team to design solutions that work best for the company. Provide thought leadership on compensation issues.
Fintech
As Principal Architect at Credit Karma, you will drive the architectural and technical strategy for the Personal Finance Platform, collaborate with engineering teams and product development, and lead architectural reviews. You will also mentor and grow engineers, foster innovation, and engage with external partners to develop a holistic strategy.
Fintech
Lead the Product Marketing function at Credit Karma, define scope and key KPIs, partner with stakeholders across Intuit, own product marketing fundamentals, provide world-class deliverables, and inspire action through data-driven insights.
Fintech
The Director of Data Science at Credit Karma will lead a team of data scientists and managers in applying machine learning techniques to solve financial problems for millions of members. They will collaborate with cross-functional teams and shape the technical vision and strategy for ML capabilities. The ideal candidate has a strong background in AI development, ML operational excellence, and data product ecosystem understanding.