Senior Data Scientist, Algorithms - Pricing, Rider Engagement at Lyft, Inc.

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

01 Jan, 26

Salary

185000.0

Posted On

03 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Optimization Models, Statistical Models, Machine Learning, Production Code, SQL, Causal Inference, LTV Modeling, Econometrics, User Choice Modeling, Collaboration, Communication, Problem Solving, Data Visualization, Experiment Design, Technical Guidance

Industry

Ground Passenger Transportation

Description

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. The Marketplace team at Lyft is responsible for accelerating the growth of the business while simultaneously delivering on key business and financial metrics, and Rider Engagement is an integral part of that. Rider Engagement team is a fast growing team responsible for developing scalable systems and engagement levers to efficiently drive both short term and long term business outcomes. We are looking for fast learning, data-driven individuals who are passionate about tackling a variety of problems by leveraging optimization and inference to shape critical business decisions. In this role, you will work closely with Product and Engineering on a cross-functional team to prototype and build end-to-end incentive levers and budget allocation models 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, colleagues in the pricing team, and with external teams to formalize problems mathematically and within the business context Perform complex data analysis to gain a deeper understanding of problems by identifying their root causes Develop and fit statistical, machine learning, or optimization models Write production code; collaborate with Software Engineers to ship models to production Design, implement, and analyse different types of experiments, and facilitate and foster data-driven and informed decision making and prioritization Analyze experimental and observational data; facilitate launch decisions Communicate findings to a broad audience consisting of Product, Engineering, and executive leadership Provide coaching and technical guidance for other teammates Experience: Ph.D. in Operations Research, Economics, Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience 1-4+ years of professional experience with Proven experience with building and evaluating optimization models Passion for solving unstructured and non-standard mathematical problems End-to-end experience with data, including querying, aggregation, analysis, and visualization Proficiency with Python, or another interpreted programming language like R or Matlab Proficiency in SQL - able to write structured and efficient queries involving multiple large data sets Ability to collaborate and communicate with others to solve a problem Strong oral and written communication skills, and ability to collaborate with cross-functional partners Experience in causal inference, LTV modeling, econometrics, or user choice modeling 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 $148,000 - $185,000. 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. Total compensation is 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.

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Responsibilities

The Senior Data Scientist will partner with various teams to formalize problems mathematically and perform complex data analysis. They will develop and fit statistical, machine learning, or optimization models and communicate findings to a broad audience.