Staff Data Scientist, Funnel Analytics & Optimization

Mulliganfunding; San Francisco (CA); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Medical, vision, and dental

Flexible Spending Accounts (FSA)

401(k) with matching

Paid time off

Paid parental leave

Lifestyle Spending Account (LSA)

Life and AD&D policy

Learning stipend

Employee Assistance Program (EAP)

Travel assistance

Snacks in breakroom

Referral program

Company events

Job summary

Mulligan Funding is seeking a Staff Data Scientist focused on funnel analytics and optimization. You will analyze end-to-end funnel performance from acquisition to funding, identify friction points, and partner with Risk, Operations, Product, and Analytics to drive improvements while preserving risk discipline and customer experience.

The role emphasizes designing analytical frameworks, running experimentation, and translating insights into actionable recommendations for leadership.

Qualifications

• 5+ years of analytics or data science experience in fintech or data-driven environments.
• 3+ years analyzing customer funnels, conversion metrics, or operational workflows.
• Experience building, deploying, and monitoring ML models; MLOps preferred.

Responsibilities

• Analyze end‑to‑end funnel performance across acquisition, underwriting, and funding stages to identify trends and inefficiencies.
• Design and maintain metrics, dashboards, and frameworks to monitor funnel health and drop-offs.
• Identify root causes of attrition using quantitative analysis and cohort methods.

Skills

Analytics

Funnel analysis

ML models

MLOps

Cross-functional collaboration

Experimentation

Education

Master's degree in Data Science/Statistics/Economics/Finance

Job description

Mulligan Funding is seeking a Staff Data Scientist focused on funnel analytics and optimization. You will analyze end-to-end funnel performance from acquisition to funding, identify friction points, and partner with Risk, Operations, Product, and Analytics to drive improvements while preserving risk discipline and customer experience.

The role emphasizes designing analytical frameworks, running experimentation, and translating insights into actionable recommendations for leadership.