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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 at the heart of Lyft’s products and decision-making. Data Scientists at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.
We are seeking a Data Science Manager to translate data into the actionable insights and algorithms that improves Rider App experiences our rider loves. In this role, you’ll shape the vision and drive execution across conversion, personalization, and platform health, ensuring we build durable relationships with every rider. By partnering with cross-functional leaders in Pricing, Loyalty, and ML teams, you will evolve our platform for both existing riders and expanding to serve new segments (e.g., Lyft Silver, Teens).
This is a high-visibility, high-impact role with direct influence on Lyft’s ride experience across millions of riders and rides everyday. The ideal candidate will bring deep expertise in advanced analytics, machine learning, causal inference, experimentation; strong business acumen in two-sided marketplace contexts; and a proven track record of leading product data science teams in fast-paced, cross-functional environments.
Responsibilities:
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Lead, mentor and grow a high-performing team of Data Scientists and Analytics, focusing on improving end-to-end Rider App experience (booking → waiting → pickup → in-ride → post-ride) with algorithm development, machine learning, experimentation and advanced analytics.
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Partner with Product Managers and Engineering leads to define the vision and roadmap for the Rider App experience (e.g., personalized UI, merchandising strategy), ensuring alignment with overall business strategy.
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Lead and execute the data science vision and roadmap for initiatives across Rider App Experience. Raise the bar on scientific rigor.
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Establish robust experimentation and causal inference frameworks to measure the business impact of new features in a two-sided marketplace.
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Conduct deep analyses of complex, large-scale datasets to uncover opportunities for growth, operational efficiency, and improved rider experience.
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Translate technical findings into actionable business insights for executive leadership.
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Champion data-driven decision-making, ensuring that product and strategy decisions are informed by rigorous quantitative analysis.
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Drive innovation by staying current with emerging research, technologies, and industry best practices in AI powered data science workflow.
Experience:
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Master’s or PhD in a quantitative field (Statistics, Applied Math, Economics, Computer Science, Operations Research) or equivalent practical experience.
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5+ years of progressive experience in data science, machine learning, optimization, or causal inference, including building complex science framework to guide critical business decisions
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2+ years of people management experience leading high-performing technical teams, with a proven ability to mentor, develop, and retain top talent.
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Demonstrated ability to set a strategic vision for data science and translate it into impactful, scalable solutions that drive measurable business outcomes.
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Deep expertise in experimental design, causal inference, machine learning, and statistical methodologies, with a track record of applying them to high-stakes product or marketplace decisions.
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Experience navigating complex, ambiguous problem spaces and guiding teams through prioritization, tradeoffs, and execution.
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Strong communication and influence skills, with the ability to engage both technical and executive stakeholders, align priorities, and build consensus.
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Hands-on proficiency with SQL, Python, large-scale data processing tools and machine learning frameworks
Preferred Qualifications
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Experience with consumer mobile apps, marketplaces, or two-sided platforms.
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Familiarity with personalization, content recommendation, uplift modeling, or lifecycle management.
Benefits:
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Extended health and dental coverage options, along with life insurance and disability benefits
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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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Access to a Lyft funded Health Care Savings Account
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RRSP plan with company match to help save for your future
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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
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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.
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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 Canada area is CAD $172,000 - CAD 215,000, 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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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
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