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

Prospect previously approved and partially completed financing customers through phone, SMS, and email outreach. Assess business goals, present funding options, overcome objections, create urgency, and convert leads into completed applications. Maintain high-volume outbound activity, track pipeline and outcomes in Salesforce, meet revenue and KPI targets, and collaborate with sales, marketing, and product teams to improve funnel performance.

Re-engage previously paid-in-full small-business clients and convert them into new funding opportunities through high-volume outbound calls, SMS, and email. Manage Salesforce lead queues, maintain accurate funnel records, meet activity and revenue quotas, analyze conversion performance, and collaborate with sales, processing, underwriting, collections, and accounting teams. The role also mentors junior representatives and pilots new dialer and AI documentation technologies.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Lead the design and execution of global total rewards programs, including compensation bands, benefits strategy, equity and incentive plans, job leveling, pay equity, and annual compensation cycles. Advise executives, HR business partners, and talent acquisition using market data, financial modeling, and internal equity analysis. Optimize total rewards systems and programs across a growing global organization.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Conduct manual penetration tests and secure code reviews across web applications, APIs, AWS infrastructure, and AI systems. Develop AI-assisted security tooling, test LLM applications and agents, triage SAST findings, tune detection rules, support security reviews before production, and communicate risks and remediation priorities to engineering teams.

Leads the Enterprise PMO, establishing intake, governance, reporting, risk, dependency, resource planning, budgeting, and portfolio review practices. Partners with Product, Engineering, Analytics, Finance, and executives to prioritize initiatives, manage capacity, surface delivery risks, and communicate portfolio performance. Manages and develops project managers, builds PMO operating maturity, and uses AI to automate Product Operations workflows.

Drive high-volume outbound re-engagement of paid-in-full merchants to generate new funding opportunities. Use CRM (Salesforce) and auto-dialer tools, analyze performance metrics, mentor junior reps, collaborate with cross-functional teams, pilot beta technologies, and meet strict activity, conversion, and quota targets.

Manage a portfolio of small and medium business customers to grow relationships and identify additional capital solutions. Analyze business and financial histories, own the sales process from approval to close, use data to prioritize opportunities, and meet monthly quota in a fast-paced environment.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.