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Staff Applied Scientist

Open 21d posting dated 2 weeks ago

Summary

Develops and deploys machine learning and optimization models to power pricing and ETA decisions for Lyft's marketplace, working with cross-functional teams to solve real-world problems at scale.

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 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. You will leverage your skills to build ML and optimization models and productionalize pipelines that can scale to millions of calls per day while solving critical business problems that have a big impact on the marketplace and rider experience. You will get exposure to a diverse set of real-world problems across optimization, prediction, machine learning, and inference and collaborate closely with teammates and stakeholders across Pricing, from Product Managers to Engineers and Analysts.

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.

Responsibilities

  • Partner with Data Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context
  • Write production quality code. Design, build and deploy production-grade ML and Optimization models. Able to build custom methods and tooling beyond off-the-shelf libraries.
  • Perform data analysis and build proof-of-concepts to explore and propose ML and Optimization solutions to both new and existing problems.
  • Evaluate machine learning systems against business goals. Collaborate with Engineers to implement algorithms in live systems and ensure the robustness of the systems
  • Establish metrics and development measurement methodologies to monitor the health of our products, as well as the impacts on user and marketplace outcomes
  • Drive collaboration and coordination with cross-functional teams

Experience

  • M.S. or Ph.D. in Machine Learning, Operations Research, Statistics, Computer Science or other quantitative fields
  • 2+ years of algorithms experience in a technology company setting
  • Proficiency with Python and working in a production coding environment
  • Passion for solving unstructured and non-standard mathematical problems and building impactful machine learning models leveraging expertise in one or multiple fields.
  • Strong understanding of machine learning methodologies, with proven experience with building and evaluating optimization or machine learning models
  • Strong verbal and written communication skills with a good track record of collaborating with others to solve a problem

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
  • In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
  • 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
  • Subsidized commuter benefits
  • Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $193,600 - $242,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

What this application asks

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First Name, Last Name, Email, Phone, Resume/CV, Location

  • Please enter your relevant employment and military service above using the + Add Another Employment link. choose one
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  • Can you perform these essential functions of the job with reasonable accommodation? choose one
  • Please describe any need for a reasonable accommodation for this hiring process (no documentation required). optional
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  • LinkedIn Profile optional
  • Have you been employed by Lyft, or any subsidiary, affiliate, or business unit of Lyft, in the past (whether on a full-time or part-time basis)? choose one
  • Zip / postal code
  • This position is based in the United States. Do you currently reside in commutable proximity to a Lyft Office located in San Francisco or are you open to relocating? choose one
  • I certify that the facts set forth in this Application for Employment are true and complete to the best of my knowledge. I understand that if I am employed, false statements, omissions or misrepresentations may result in my dismissal. I authorize the Employer to make an investigation of any of the facts set forth in this application and release the Employer from any liability. The employer may contact any provided references.

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