Data Scientist, Algorithms - Pricing - Rider Engagement

Overview

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