Base: $140,800 - $176,000; bonus/equity: not speci...
On-site
Develop mathematical models
Launch algorithms
Build ml and optimization models
As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions
Job Summary
As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions.
We are looking for someone who is excited about working in a fast-paced, innovative, and impactful environment, and is adept at balancing complexity and efficiency to translate real world business problems into reliable solutions, systems and decision frameworks.
Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging.
Matching Summary
As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions.
Salary
Base: $140,800 - $176,000; Bonus/Equity: Not specified; Benefits: Great medical, dental, and vision insurance options with additional programs available when enrolled, Mental health benefits, Family building benefits, Child care and pet benefits, 401(k) plan with company match to help save for your future, discretionary paid time off, 18 weeks of paid parental leave, Subsidized commuter benefits, Monthly Lyft credits and complimentary Lyft Pink membership
Skills & Requirements
Must-have
Develop mathematical models
Launch algorithms
Build ML and optimization models
Productionalize pipelines
Write production quality code
Evaluate machine learning systems
Nice-to-have
Fast-paced, innovative, impactful environment
Balancing complexity and efficiency
Translate real world business problems
Key Requirements
M.S. or Ph.D. in quantitative fields
2+ years of algorithms experience
Proficiency with Python
Experience building and evaluating optimization or machine learning models