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Data Scientist - Decisions, Mapping

Lyft

Data Scientist - Decisions, Mapping

Lyft

Toronto, Canada

·

On-site

·

Full-time

·

5d ago

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 on the Mapping team, you will collaborate with our world class team of engineers, product managers, and designers to grow and improve the quality of recommended routes and accuracy of our travel time estimations. We're looking for a passionate, driven Data Scientist who is excited to dive into our spatial data and build a best-in-class mapping product that provides safe, efficient, and seamless navigation for our rideshare drivers.

Data Science is at the heart of Lyft’s products and decision-making. You will leverage data and rigorous, analytical thinking to shape our mapping products and make business decisions that put our customers first. This will involve identifying and scoping opportunities, shaping priorities, recommending technical solutions, designing experiments, and measuring the impact of new features. You will help us solve some of the most impactful problems in mapping, including:

  • How do we improve the quality of our map data in order to improve our recommendations?

  • How do we benchmark and measure the success of our services?

  • How do we validate features of the real world that affect our routing algorithms?

  • Are we meeting our travel estimation promises to our customers?

Responsibilities

  • Leverage data and analytic frameworks to identify opportunities for growth and efficiency

  • Partner with product managers, engineers, and operators to translate analytical insights into decisions and action, and implement products to drive business goals

  • Define and implement decision frameworks, measurement strategies, and scientific methodologies that bring consistency and rigor to business decisions and forecasts, balancing opportunity and uncertainty

  • Deliver integrated, high-quality analytical outputs spanning multiple projects while navigating ambiguity, cross-team dependencies, and open-ended scope

  • Design and analyze online experiments; communicate results and act on launch decisions

  • Establish metrics that measure the health of our products, as well as rider and driver experience

  • Identify and drive impact and alignment, shaping product and business strategy through data-centric presentations

  • Contribute to the Science community (hiring, onboarding, documentation, knowledge-sharing, tooling improvements), helping make Decision Science at Lyft more effective and scalable

Experiences

  • Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience

  • 3+ years experience in a data science role or analytics role

  • Demonstrated ability to own multi-project analytical scopes with ambiguous problem definitions and cross-functional integration

  • End-to-end experience with data, including querying, aggregation, analysis, and visualization

  • 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, and machine learning libraries such as Py Torch, Tensor Flow, Keras

  • Experience in online experimentation and statistical analysis, and communicating results and recommendations to senior stakeholders

  • Strong communication, critical thinking, and prioritization skills, including the ability to challenge assumptions, propose alternatives, and balance short-term vs. long-term tradeoffs

  • Experience in applying machine learning techniques is a plus (e.g. reinforcement learning) to solve customer problems (e.g. personalization, segmentation)

  • Expertise in metric design, causal analysis, behavioral analytics, decision frameworks and measurement strategy is a plus

  • Experience working with ETL pipelines a plus

Benefits:

  • Extended health and dental coverage options, along with life insurance and disability benefits

  • Mental health benefits

  • Family building benefits

  • Child care and 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 $108,000 - $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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About Lyft

Lyft

Lyft

Public

Lyft, 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