Jobs Companies Lyft Data Scientist, Decisions - Payments

Über diese Data Scientist, Decisions - Payments Stelle bei Lyft

Lyft · Hybrid · Toronto, Canada

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 a Data Scientist in the Pay, Integrity & Identity org, you will collaborate with our world class team of engineers, product managers, analysts and other data scientists to help create best in class pay platforms, stop fraudulent actors from harming our riders & drivers fraud and build user trust on the Lyft platform. You will run experiments (A/B tests) and develop data driven solutions to launch new features and remove the bad actors from the Lyft platform while maintaining a positive experience for genuine users. We’re looking for an intellectually curious individual who has extraordinary attention to detail, a track record of analytical problem-solving and skilled communication.

Prior experience in the fintech, fraud or identity space is preferred.

Responsibilities: 

  • Design and analyze experiments in collaboration with other scientists, product & engineering; communicate findings to stakeholders and facilitate launch decisions
  • Leverage advanced statistical techniques to generate quantitative insights and develop machine learning models
  • Analyze the wide variety of signals available to identify patterns in large datasets and uncover root causes
  • Partner with product managers, engineers, and operators to translate analytical insights into decisions and action
  • Build data pipelines and develop analytical frameworks to monitor business and product performance
  • Set business metrics that measure the health of our products, as well as passenger and driver experience
  • Collaborate with product and engineering and communicate findings to stakeholders in a clear and concise manner

Experience: 

  • Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience
  • 4-6+ years of industry experience in a data science or analytical role. 
  • Proficiency in SQL - able to write structured and efficient queries on large data sets
  • Experience in programming, especially with data science and visualization libraries in Python or R
  • Strong oral and written communication skills, and ability to collaborate with and influence cross-functional partners

Benefits:

  • Extended health and dental coverage options, along with life insurance and disability benefits
  • Mental health benefits
  • Family building benefits
  • Pet benefits
  • Access to a Lyft funded Health Care Savings Account
  • RRSP plan with company match to help save for your future
  • In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
  • Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
  • Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

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 at least 3 days per week, including 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 Toronto area is CAD $108,000 - CAD $135,000, 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.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.

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Wie sich dieses Gehalt für Data Scientist vergleicht

Diese Stelle zahlt $108,000/yrunter der üblichen Spanne für Data Scientist Stellen.

$100,000 dem Median $142,714 $195,000

Übliche Spanne $108,000–$177,048/yr, aus 11 vergleichbaren Data Scientist Anzeigen auf JobsRadar (Vergütung auf USD hochgerechnet). Gehaltseinblicke für Data Scientist ansehen →

Über Lyft

Wherever you’re headed, count on Lyft for rides in minutes. The Lyft app matches you with local drivers at the tap of a button. Just request and go. Ride by ride, we’re changing the way our world works. We imagine a world where cities feel small again. Where transportation and tech bring people together, instead of apart. We see the future as community-driven — and it starts with you.

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