Jobs
Benefits & Perks
•Wellness benefits
•Flexible PTO policy
•Annual team offsites
•Health, dental, and vision coverage
•Top Tier compensation with equity
Required Skills
TensorFlow
SQL
Apache Spark
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.
Data Science is central to Lyft's products and decision-making. As a Data Scientist on the cross-functional team, you will work in a dynamic environment, tackling a variety of problems from shaping critical business decisions to building algorithms that power our products. We seek passionate, driven Data Scientists to address some of the most interesting and impactful problems in ridesharing.
As a Data Scientist specializing in Algorithms, you will develop mathematical models for the platform's core services, addressing diverse problems in optimization, prediction, machine learning, and inference. On the Fulfillment team, you will collaborate with cross-functional teammates and stakeholders to enhance algorithms for matching rideshare supply and demand in real time and develop product offerings to improve the experiences of Lyft Riders and Drivers.
Responsibilities:
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Leverage data and analytic frameworks to direct creations and improvements of algorithms and models underpinning the team’s systems and products
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Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context.
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Perform exploratory data analysis to gain a deeper understanding of the problem
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Construct and fit statistical, machine learning, or optimization models
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Write production modeling code; collaborate with Software Engineers to implement algorithms in production
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Design and implement both simulated and live traffic experiments
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Analyze experimental and observational data; communicate findings; facilitate launch decisions
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Develop measurement methodologies to monitor the health of our products, as well as the impacts on user outcomes and marketplace outcomes
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Drive collaboration and coordination with cross-functional teams
Experience:
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M.S. or Ph.D. in Machine Learning, Statistics, Operations Research, Computer Science, Mathematics, or other quantitative fields
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2+ years professional experience in a technology company setting
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Proven experience with building and evaluating machine learning models
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Proficiency with Python and working in a production coding environment
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Passion for solving unstructured and non-standard mathematical problems
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End-to-end experience with data, including querying, aggregation, analysis, and visualization
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Strong oral and written communication skills, and ability to collaborate and communicate with others to solve a problem
Benefits:
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Great medical, dental, and vision insurance options with additional programs available when enrolled
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Mental health benefits
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Family building benefits
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Child care and pet benefits
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401(k) plan to help save for your future
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In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
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18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
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Subsidized commuter benefits
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Lyft Pink
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Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program
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 Seattle area is $128,000 -$160,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
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About Lyft

Lyft
PublicLyft, Inc. is an American company offering ride-hailing services, motorized scooters, and bicycle-sharing systems in the United States and Canada, and, via its Free Now mobile app, Europe. Lyft is the second-largest ridesharing company in the United States after Uber.
1,001-5,000
Employees
San Francisco
Headquarters
$3.2B
Valuation
Reviews
2.7
10 reviews
Work Life Balance
3.8
Compensation
2.1
Culture
2.3
Career
2.0
Management
1.8
25%
Recommend to a Friend
Pros
Flexible scheduling and work-from-home options
Easy money and side hustle opportunities
Meet new people with good conversations
Cons
Unfair pay structure and low compensation
Easy deactivation based on rider complaints
Poor customer support for drivers
Salary Ranges
29 data points
Mid/L4
Senior/L5
Mid/L4 · Data Engineer
2 reports
$191,262
total / year
Base
$147,125
Stock
-
Bonus
-
$169,325
$213,200
Interview Experience
5 interviews
Difficulty
4.0
/ 5
Duration
14-28 weeks
Offer Rate
100%
Experience
Positive 60%
Neutral 40%
Negative 0%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Technical Interview/Take-home Challenge
5
System Design Interview
6
Onsite/Virtual Interviews
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
News & Buzz
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