Senior Data Scientist, Product

Mudflap•Palo Alto, CA

2d•$185,000 - $225,000•Hybrid

About The Position

Mudflap is seeking a Senior Data Scientist, Product to help build a data-driven decision engine across product development, customer experience, growth, and operations. The role partners with Product, Engineering, Operations, Risk, and company leadership to guide strategic product decisions through advanced analytics, experimentation, and machine learning. The position is based in Palo Alto, California, with a hybrid approach balancing in-office collaboration and remote work. The Senior Data Scientist will lead machine learning projects from problem formulation through production deployment, develop and evaluate models, build data products, run experiments, and identify actionable insights that shape product strategy and business outcomes. The role also provides technical leadership and mentorship, establishes data science practices, and helps teams use experimentation and analysis to improve key business metrics.

Requirements

At least 5 years of experience in data science, analytics, or a related field.
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, including A/B testing, cohort analysis, and causal inference.
Excellent communication skills, including the ability to present complex insights clearly to technical and non-technical stakeholders.
Comfort working in a fast-paced startup environment where collaboration and flexibility are essential.

Nice To Haves

Experience in at least one of the following domains: risk, growth or marketing, or product analytics.

Responsibilities

Drive strategic data science initiatives that affect business outcomes and identify high-value opportunities in the role’s domain in partnership with executive leadership.
Lead end-to-end machine learning projects, from problem formulation through production deployment, including model development, validation, A/B testing, and performance monitoring.
Address business problems such as fraud risk assessment, underwriting, and customer segmentation.
Collaborate with Product, Engineering, Operations, and Risk teams to translate business requirements into analytical solutions and communicate insights to executive stakeholders.
Mentor junior data scientists and analysts, provide technical leadership and code reviews, and establish scalable data science methodologies and standards.
Lead experimentation strategy and end-to-end A/B testing frameworks, guide teams on experimental design, and translate results into product improvements that affect key business metrics.
Design and analyze experiments, develop predictive models, build data products, and uncover insights to support product decisions and business outcomes.