채용
복지 및 혜택
•Learning
•Parental Leave
•Unlimited Pto
필수 스킬
SQL
Python
Airflow
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 New York City 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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총 지원 클릭 수
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모의 지원자 수
0
스크랩
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비슷한 채용공고
Lyft 소개

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
직원 수
San Francisco
본사 위치
$3.2B
기업 가치
리뷰
3.9
10개 리뷰
워라밸
3.8
보상
3.2
문화
4.2
커리어
3.5
경영진
3.7
72%
친구에게 추천
장점
Flexible hours/schedule
Great team culture
Good work-life balance
단점
Long hours
Fast-paced work environment
Pay could be better
연봉 정보
41개 데이터
Mid/L4
Senior/L5
Mid/L4 · DATA SCIENTIST
10개 리포트
$166,400
총 연봉
기본급
$128,000
주식
-
보너스
-
$166,400
$229,600
면접 경험
2개 면접
난이도
3.0
/ 5
소요 기간
14-28주
합격률
50%
면접 과정
1
Application Review
2
Recruiter Screen
3
Technical Assessment
4
Technical Interview
5
Onsite/Virtual Interviews
6
Offer
자주 나오는 질문
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
뉴스 & 버즈
I'm an ex-Wall Street trader who drives for Uber and Lyft. Gas prices have me rethinking which trips I take. - Business Insider
Business Insider
News
·
3d ago
Why Is Lyft Stock Crashing, and is it a Buying Opportunity? - The Motley Fool
The Motley Fool
News
·
3d ago
Uber and Lyft launch new shuttle service to Boston's Logan Airport - WBUR
WBUR
News
·
4d ago
Uber, Lyft now offering shuttles to and from Logan Airport - WHDH
WHDH
News
·
4d ago




