Data Scientist, Algorithms - Pricing - Rider Engagement

About the Role

Data Science is at the heart of Lyft’s products and decision-making. Data Scientists at Lyft operate in a dynamic environment, partnering closely with cross-functional teammates to tackle open-ended business problems and build transportation solutions.

The Pricing team determines prices for all rideshare products and supports new initiatives, with Rider Engagement driving short- and long-term business outcomes through algorithms and systems behind incentive offers, budget management, operational control, and real-time optimization.

In this role, you will work closely with Product, Engineering, and Analytics to develop and iterate incentive levers, prototype and build end-to-end solutions that operate at scale, and influence existing and future roadmaps.

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 system robustness.
• Develop and fit statistical, machine learning, or optimization models. Deploy production-grade models that solve business problems.
• Design, implement, and analyze different types of experiments, facilitating data-driven decision-making and prioritization.
• Establish metrics and measurement methodologies to monitor product health and marketplace outcomes.
• Perform deep-dive analysis to gain understanding and uncover new product and business opportunities.
• Drive collaboration and coordination with cross-functional teams, communicating findings to Product, Engineering, and executive leadership.

Experience & Qualifications

• M.S. or Ph.D. in Machine Learning, Operations Research, Statistics, Computer Science, or other quantitative fields.
• 1-4+ years of professional experience for PhDs or 3-5+ years for Master’s in a Data Science role in a technology company setting.
• Proven experience 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 to solve problems.