Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Provides financial leadership for global product and channel partnerships exceeding $30 million in revenue. Responsibilities include forecasting, performance reporting, margin improvement, partner economics, financial modeling, commercial deal evaluation, risk assessment, and governance. The role partners with senior leaders and regional FP&A teams to improve financial visibility, assess investments, and support strategic partnership decisions across technology, AI, and SaaS environments.

Advise Experian’s Financial Services and Data division on regulatory compliance risks related to consumer credit services. Conduct policy gap assessments, develop remediation plans, support regulatory examinations, analyze requested documentation, and coordinate with Legal, Information Security, Governmental Affairs, and business teams. Help improve control effectiveness, resolve compliance issues, and prepare materials for regulatory meetings.

Advise business teams on consumer credit regulatory compliance, perform policy and procedure gap assessments, track remediation, support regulatory examinations, analyze requested documentation, and help prepare regulatory meeting materials. The role collaborates with Legal, Information Security, Governmental Affairs, and business units to address compliance risks, improve controls, and resolve complex issues involving privacy and consumer protection laws.

Lead a portfolio of credit-based, data-driven consumer prospecting products for financial institutions. Define product strategy, requirements, user stories, positioning, pricing, and investment priorities while partnering with engineering, sales, legal, compliance, marketing, operations, and executives. Represent the product line internally and externally, support revenue growth, and help advance Experian’s Ascend platform strategy.

Define and execute the North America B2B email and identity data product strategy and roadmap. Lead product launches, prioritize opportunities using customer and market insights, monitor competition and emerging data use cases, and develop partnerships and integrations. Collaborate with Sales, Marketing, Support, regional and global teams on go-to-market plans and adoption. Manage people, engage senior stakeholders and clients, and represent products through thought leadership.

Leads technical pre-sales activities for enterprise software, including platform demonstrations, solution design, integration discussions, client requirement translation, and formal handoffs to delivery teams. Collaborates with Sales, Product Management, and Client Services; documents workflows, maintains pipeline updates, identifies platform use cases, creates enhancement tickets, and supports acceptance testing.

Designs and evolves enterprise data and AI platform architecture, enabling privacy-preserving cross-border data products, entity resolution, semantic standards, governed discovery, and AI-agent data access. Leads prototyping, reference implementations, technology adoption, and platform architecture decisions across engineering teams. Mentors engineers while applying expertise in distributed systems, cloud infrastructure, data modeling, governance, graph databases, and production operations.

Support enterprise clients through product onboarding, implementation, and ongoing technical assistance. Lead client presentations, troubleshoot production and data-processing issues, manage technical support tickets, and collaborate with Client Services, Sales, and Operations. Develop product expertise and serve as a key client contact through calls and high-touch communication.

Develop fraud detection models and predictive features for account opening, account takeover, and identity risk. Responsibilities include investigating large datasets, identifying fraud patterns, engineering features, training and evaluating machine learning models, writing tested Python and PySpark code, supporting production deployment, monitoring model performance and drift, and presenting analytical recommendations. The role also requires adherence to data privacy, explainability, model validation, documentation, and governance standards.

Build and evaluate machine learning models and features for fraud detection across account opening, account takeover, and identity risk. Analyze complex datasets to identify fraud patterns, develop hypotheses, validate models, and measure technical and business performance. Write tested Python code, support production deployment and monitoring, communicate findings, and follow standards for privacy, explainability, validation, documentation, and governance.