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Data Scientist

LyftSeattle, WA Posted 2mo ago

Workplace
Hybrid
Type
Full-time
Level
Not stated
Salary
Not listed
SkillsPythonMachine LearningData & AI

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.

Responsibilities:

  • Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context.
  • Perform exploratory data analysis to gain a deeper understanding of the problem.
  • Construct and fit statistical, machine learning, or optimization models.
  • Write production modeling code.
  • Collaborate with Software Engineers to implement algorithms in production.
  • Design and run both simulated and live traffic experiments.
  • Analyze experimental and observational data.
  • Communicate findings.
  • Facilitate launch decisions.

Experience:

  • Master’s degree or foreign equivalent in Mathematics, Statistics, Economics, or related field.
  • 2 years of experience in job offered, Operations Research, or related occupation.
  • Experience with solving unstructured and non-standard mathematical problems.
  • Experience with end-to-end data including querying, aggregation, analysis, and visualization.
  • Experience with Python and other programming languages such as R and MATLAB.

Benefits:

  • Great medical, dental, and vision insurance options with additional programs available when enrolled
  • Mental health benefits
  • Family building benefits
  • Child care and pet benefits
  • 401(k) plan to help save for your future
  • In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
  • 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
  • Subsidized commuter benefits
  • Lyft Pink - 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 $129,709 - $192,000/year. 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. #LI-DNI

JobsPing insights

Our own analysis of this role, based on every open job we track. Updated daily.

Salary benchmark

Data & AI roles in United States · 76 posted USD salaries

25th: $265kMedian $310k75th: $396k

This listing doesn’t publish a USD salary. Use this range as a starting point when you discuss pay. How to negotiate

How in-demand these skills are

Share of all open roles on JobsPing that ask for each skill

Lyft right now

Open roles
153
Remote
1%
New this week
5

Skills they ask for most: Python, SQL, Machine Learning, AWS.

How to stand out for this role

  • Quantify model or analysis impact in business terms (conversion, cost, accuracy lift), not just metrics like AUC.
  • Show the full pipeline you owned: data sourcing, cleaning, modelling, deployment and monitoring.
  • Have a short notebook or write-up ready that explains a trade-off you made and why.
  • List the specific warehouse and orchestration tools you’ve used (e.g. Snowflake, dbt, Airflow).
Our 20-minute tailoring method

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