Data Scientist, Decisions - Payments

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

As a Data Scientist in the Pay, Integrity & Identity org, you will collaborate with our world class team of engineers, product managers, analysts and other data scientists to help create best in class pay platforms, stop fraudulent actors from harming our riders & drivers fraud and build user trust on the Lyft platform. You will run experiments (A/B tests) and develop data driven solutions to launch new features and remove the bad actors from the Lyft platform while maintaining a positive experience for genuine users. We’re looking for an intellectually curious individual who has extraordinary attention to detail, a track record of analytical problem-solving and skilled communication.

Prior experience in the fintech, fraud or identity space is preferred.

• →Design and analyze experiments in collaboration with other scientists, product & engineering; communicate findings to stakeholders and facilitate launch decisions
• →Leverage advanced statistical techniques to generate quantitative insights and develop machine learning models
• →Analyze the wide variety of signals available to identify patterns in large datasets and uncover root causes
• →Partner with product managers, engineers, and operators to translate analytical insights into decisions and action
• →Build data pipelines and develop analytical frameworks to monitor business and product performance
• →Set business metrics that measure the health of our products, as well as passenger and driver experience
• →Collaborate with product and engineering and communicate findings to stakeholders in a clear and concise manner

• Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience
• 4-6+ years of industry experience in a data science or analytical role. 
• Proficiency in SQL - able to write structured and efficient queries on large data sets
• Experience in programming, especially with data science and visualization libraries in Python or R
• Strong oral and written communication skills, and ability to collaborate with and influence cross-functional partners