Applied Scientist: Real-Time Pricing & Optimization

Lyft; San Francisco (CA); Hybrid; USD 140,800 - 176,000; Full time

Benefits offered by this job

Great medical, dental, and vision insurance

Mental health benefits

401(k) plan with company match

Discretionary paid time off

18 weeks of paid parental leave

Monthly Lyft credits and complimentary Lyft Pink membership

Job summary

Lyft is looking for an Applied Scientist specializing in Machine Learning and Operations Research in San Francisco. You will develop mathematical models to optimize pricing and ETAs, collaborating closely with cross-functional teams. A strong background in ML methodologies and programming in Python is required.

The expected base pay range is $140,800 - $176,000, alongside comprehensive benefits such as health insurance and parental leave, making this a great opportunity for candidates passionate about solving complex business problems.

Qualifications

• 2+ years of algorithms experience in a technology company setting.
• Strong understanding of machine learning methodologies.
• Strong verbal and written communication skills.

Responsibilities

• Partner with teams to frame problems mathematically.
• Design, build, and deploy ML and optimization models.
• Establish metrics to monitor product health.

Skills

Machine Learning

Python

Operations Research

Data Analysis

Education

M.S. or Ph.D. in Machine Learning, Operations Research, Statistics, Computer Science

Tools

Production coding environment

Job description

Lyft is looking for an Applied Scientist specializing in Machine Learning and Operations Research in San Francisco. You will develop mathematical models to optimize pricing and ETAs, collaborating closely with cross-functional teams. A strong background in ML methodologies and programming in Python is required.

The expected base pay range is $140,800 - $176,000, alongside comprehensive benefits such as health insurance and parental leave, making this a great opportunity for candidates passionate about solving complex business problems.