Job Description
This role is open for both San Diego or San Francisco location
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Locations: San Diego or SF Bay Area
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- Looking for candidates to be based in San Diego, CA for a hybrid role (minimum 3 days in the office).
- Also open to candidates in the San Francisco Bay Area, as the client is planning to open an office there. Until that’s set up, Bay Area candidates can work remotely and will move to a Hybrid setup once the office is ready.
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Role and Responsibilities:
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As the Project Manager – Funnel Analytics & Optimization, you will own end-to-end funnel analytics across acquisition, application, underwriting, and funding for a small-business lending portfolio. You will identify friction points and operational bottlenecks, design measurement frameworks, and run test-and-learn experiments to improve conversion, cycle time, and customer outcomes—while maintaining strong risk discipline. You will partner with client and internal stakeholders across Risk, Operations, Product, Engineering, and AI teams to translate insights into scalable process changes, automation, and AI-agent solutions.
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- Analyze end-to-end funnel performance across acquisition, application, underwriting, and funding stages to identify conversion trends and structural inefficiencies
- Design and maintain analytical frameworks to monitor funnel health, conversion rates, and customer drop-off patterns
- Identify root causes of customer attrition and dropout at each funnel stage using quantitative analysis and cohort-based methodologies
- Drive a statistical approach to assessing information value of each funnel step v customer attrition and drop out
- Support test-and-learn initiatives, including A/B testing and controlled experiments, to measure the impact of funnel optimization efforts
- Partner closely with process owners & AI team to deploy automation and AI agent solutions to fundamentally enhance funnel efficiency
- Deploy game theory or other relevant statistical frameworks to enhance take up rate of client offers by channel partner
- Partner with Operations, Underwriting, and Product teams to design and evaluate strategies that reduce customer friction and improve completion rates
- Develop segmentation and behavioral analyses to identify populations with elevated dropout risk or conversion opportunity
- Translate analytical findings into clear, actionable recommendations for senior leadership and cross-functional partners
- Monitor post-implementation results to ensure sustained improvement and surface second-order impacts on risk, cost, and customer experience
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Candidate Profile:
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- 5+ years of relevant experience in analytics or data science within fintech, financial services, or a data-driven consumer or B2B environment
- SMB experience for large loans & channel partner as lead referral or some other more complex funnel like mortgage etc.
- Experience building ML models, deployment and monitoring. MLOps knowledge is also highly preferred
- Very strong DS/analytical skills to understand the process and drive analytical optimization of it
- Hands-on experience analyzing customer funnels, conversion metrics, or operational workflows
- Demonstrated experience partnering with operations or frontline teams to translate analytics into execution
- Experience supporting experimentation, measurement, and performance monitoring in production environments
- Funnel and conversion analytics
- Cohort analysis and customer journey analysis
- Drop-off and attrition modeling
- Experimentation and A/B testing
- Statistical analysis and hypothesis testing
- Cross-functional analytical storytelling
- Strong SQL and Python (or R) proficiency; ability to work with large datasets and build reproducible analytical workflows.
- Excellent communication, presentation, and story-building skills in a consulting/client-facing setup.
- Demonstrated ability to lead cross-functional initiatives end-to-end and coordinate with offshore delivery teams.
- Bachelor’s degree in related quantitative fields such as Data Science, Statistics, Mathematics, Economics, Finance, or Engineering required.
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