Job Overview
Location
Palo Alto, CA
Job Type
Full-time
Category
Data Science
Date Posted
June 13, 2026
Full Job Description
đź“‹ Description
- • Lead end-to-end machine learning projects from problem formulation through production deployment, including model development, validation, A/B testing, and performance monitoring to drive strategic pricing decisions.
- • Develop pricing and elasticity models to estimate customer willingness-to-pay and inform revenue, margin, and retention strategies for Mudflap’s fuel payment products.
- • Design and analyze pricing experiments to optimize product pricing, maximize long-term value, and accelerate growth in the $800B trucking industry.
- • Collaborate closely with Product, Finance, Revenue Operations, and Engineering teams to translate complex business requirements into analytical solutions and communicate insights to executive stakeholders.
- • Drive data-driven decision making across cross-functional teams by building scalable data science methodologies and standards that impact pricing, risk, and customer segmentation.
- • Own end-to-end A/B testing frameworks, mentor teams on experimental design, and translate test results into actionable product improvements that directly impact key business metrics.
- • Mentor junior data scientists and analysts by providing technical leadership, conducting code reviews, and establishing best practices in data science workflows.
- • Solve high-impact business problems including fraudulent risk assessment, underwriting, and customer segmentation using advanced statistical and machine learning techniques.
- • Work in a hybrid environment based in Palo Alto, CA, balancing in-office collaboration with remote work to support team alignment and innovation.
- • Partner with executive leadership to identify high-value data science opportunities that directly influence business outcomes and strategic priorities.
- • Maintain and improve production-ready models and pipelines to ensure reliability, scalability, and performance in a fast-paced startup environment.
- • Apply strong statistical knowledge in causal inference, cohort analysis, and experimental design to derive actionable insights from large-scale transactional data.
- • Communicate complex technical findings clearly to both technical and non-technical audiences to drive alignment and decision-making across the organization.
- • Contribute to a customer-obsessed culture by ensuring all pricing strategies are grounded in deep understanding of trucker and fuel stop partner needs.
🎯 Requirements
- • 5+ years of experience in data science, analytics, or related fields.
- • Proficiency in SQL and Python for data manipulation, modeling, and automation.
- • Experience building production-ready models and pipelines.
- • Strong statistical knowledge and experience designing experiments (A/B testing, cohort analysis, causal inference).
- • Excellent communication skills—ability to present complex insights clearly to technical and non-technical stakeholders.
- • Experience in at least one of the domains: risk, growth/marketing, or product analytics preferred.
🏖️ Benefits
- • Competitive salary and equity in a high-growth startup.
- • Multiple health benefit options.
- • Responsible Time Off.
- • 401(k) matching.
- • Opportunities and support for major career growth.
- • Annual Company offsite event (Mudfest!).
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Mudflap Inc.
Mudflap Inc. operates a mobile application that enables owner-operator truck drivers and small fleets to save on fuel at over 1,500 independent truck stops across the United States. Users reserve discounted fuel through the app, then pay instantly via mobile without needing fleet cards or credit checks. The company partners with fuel retailers to offer lower prices and earns revenue through merchant fees and data services. Founded in 2017 and headquartered in San Francisco, the platform also provides analytics and loyalty rewards to improve fuel purchasing efficiency for small trucking businesses.
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