At Lyft, the business problem is to cultivate a work environment where all team members belong and have the opportunity to thrive, and to serve and connect by leveraging data and rigorous, analytical thinking to shape products and make business decisions, specifically in the area of driver and rider safety.
Requirements
- Proficiency in SQL and Python
- End-to-end experience with data, including querying, aggregation, analysis, and visualization
- Ability to construct and fit statistical, machine learning, or optimization models
- Experience with designing and analyzing online experiments
- Ability to develop analytical frameworks to monitor business and product performance
- Ability to establish metrics that measure the health of our products, the business, as well as the driver experience
- Experience with data science, analytics, or management consulting
Responsibilities
- Leverage data and analytic frameworks to identify opportunities for growth and efficiency
- Construct and fit statistical, machine learning, or optimization models
- Design and analyze online experiments; communicate results and act on launch decisions
- Develop analytical frameworks to monitor business and product performance
- Establish metrics that measure the health of our products, the business, as well as the driver experience
- Partner with product managers, engineers, marketers, designers, and operators to translate data insights into decisions and action
- Design and implement experiments
Other
- Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred)
- 3+ years of industry experience in a data science, analytics, or management consulting role
- Ability to manage, influence, negotiate, and inspire others in a fast-moving environment
- Strong oral and written communication skills, and ability to collaborate with cross-functional partners
- Hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays
