Senior Data Scientist, Pricing

Mudflap•Palo Alto, CA

2d•$185,000 - $225,000•Hybrid

About The Position

Mudflap serves the $800B trucking industry, the backbone of the U.S. economy. Our market-leading payment products help truckers save thousands of dollars on fuel (their #1 business expense), while providing our fuel stop partners with access to new, hard-to-reach customers. We’re a fast-growing marketplace business looking for a new customer-obsessed teammate to join us on this exciting journey. At Mudflap, we’re building a data-driven decision engine that powers every aspect of our business—from product development and growth to risk management and operational excellence. We’re looking for a versatile Senior Data Scientist, Pricing to join our high-impact team. In this role, you’ll partner closely with Product, Finance, Revenue Operations, and Engineering to drive strategic pricing decisions that accelerate growth and maximize long-term value. You’ll develop pricing and elasticity models, estimate customer willingness-to-pay, design and analyze pricing experiments, and generate insights that inform revenue, margin, and retention strategies. Your work will directly influence how we price our products, optimize customer value, and compete in the market. This role is based in Palo Alto, CA and involves a hybrid work approach, balancing in-office collaboration with the ability to work remotely.

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.
Comfortable working in a fast-paced startup environment where collaboration and flexibility are essential.

Responsibilities

Drive strategic data science initiatives that directly impact business outcomes, working closely with executive leadership to identify high-value opportunities in your domain.
Lead end-to-end machine learning projects from problem formulation through production deployment, including model development, validation, A/B testing, and performance monitoring. solve high-impact business problems such as fraudulent risk assessment, underwriting, customer segmentation, etc.
Drive data-driven decision making across cross-functional partnerships — Collaborate closely with Product, Engineering, Ops, Risk teams, etc. to translate complex business requirements into analytical solutions and communicate insights to executive stakeholders
Mentor junior data scientists and analysts and establish best practices — Provide technical leadership, code reviews, and strategic guidance while developing scalable data science methodologies and standards across the organization
Lead experimentation strategy and drive product innovation — 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