Level

Lyft

Data Scientist, Algorithms - Pricing - Rider Engagement

Lyft is hiring a Data Scientist, Algorithms - Pricing - Rider Engagement in San Francisco, United States. It pays $128k-$160k a year and Level rates it ; you can apply on Lyft.

Level's read on this job

  • Level rates this data scientist role Builds AI ●●●●: the job is building AI systems.
  • The posting mentions Python, machine learning and statistical modeling among its tools and skills.
  • Level classes it as a junior role.
  • Lyft has 180 other open roles on Level.
  • Posted within the past week.

AI in this role

Develop algorithms, statistical models, and machine learning solutions for Lyft's pricing and rider engagement marketplace.

python
machine-learningstatistical-modelingpricing-algorithmsexperiment-designoptimization

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 a dynamic environment, and partner closely with cross-functional teammates to tackle a variety of highly open-ended business problems to build the world’s best transportation solutions.

The Pricing team is a centerpiece of Lyft’s marketplace, determining prices for all rideshare products and supporting new initiatives, and Rider Engagement is an integral part of that. Rider Engagement manages one of the most impactful marketplace and business levers, driving both short term and long term business outcomes. The team owns the algorithms and systems behind incentive offers, budget management, operational control and real-time optimization.

We are looking for fast learning, data-driven individuals who are passionate about tackling these open-ended problems in pricing theory and making a large, positive impact in the business. In this role, you will work closely with Product, Engineering and Analytics on a cross-functional team to develop and iterate incentive levers, prototype and build end-to-end solutions that can operate at scale. In addition, you will be expected to leverage your domain knowledge and influence existing and future roadmaps to help Lyft continue to deliver a better experience for riders and drivers.

Responsibilities:

  • Partner with other Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context
  • Write production code. Collaborate with Software Engineers to implement algorithms in live systems and ensure the robustness of the systems
  • Develop and fit statistical, machine learning, or optimization models. Deploy production-grade models that solve business problems.
  • Design, implement, and analyse different types of experiments, and facilitate data-driven and informed decision making and prioritization
  • Establish metrics and development measurement methodologies to monitor the health of our products, as well as the impacts on user and marketplace outcomes
  • Perform deep dive analysis to gain a deeper understanding of the problem and uncover new product and business opportunities
  • Drive collaboration and coordination with cross-functional teams. Communicate findings to a broad audience consisting of Product, Engineering, and executive leadership.

Experience:

  • M.S. or Ph.D. in Machine Learning, Operations Research, Statistics, Computer Science or other quantitative fields
  • 1-4+ years professional experience for PhDs or 3-5+ years for Master’s in a Data Science role in a technology company setting
  • Proven experience with building and evaluating optimization or machine learning models
  • Proficiency with Python and working in a production coding environment
  • Passion for solving unstructured and non-standard mathematical problems
  • End-to-end experience with data, including querying, aggregation, analysis, and visualization
  • Strong verbal and written communication skills, and ability to collaborate and communicate with others to solve a problem

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 San Francisco area is $128,000-160,000/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.

How we rate this

Data Scientist, Algorithms - Pricing - Rider Engagement at Lyft rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Machine learningStatistical ModelingPricing AlgorithmsExperiment DesignOptimizationPython

Also common in data scientist jobs at this level

Pay

This job starts at
$128k
Typical data scientist job
$166k

This posting starts 23% below the typical data scientist job. See data scientist jobs

Questions you could be asked

  1. Tell me about a project where machine learning was part of your work. What did you do?
  2. Tell me about a project where statistical modeling was part of your work. What did you do?
  3. Tell me about a project where pricing algorithms was part of your work. What did you do?
  4. Tell me about a project where experiment design was part of your work. What did you do?
  5. Tell me about a project where optimization was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Machine learning, Statistical Modeling, Pricing Algorithms, Experiment Design, and Optimization. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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