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 work in dynamic environments, where we embrace moving quickly to build the world’s best transportation. We take on a variety of problems ranging from shaping long-term business strategy with data, making short-term critical decisions, and building algorithms/models that power our internal and external products.
The Real-Time Supply Management (RTSM) team’s mission is to improve rideshare market throughput by efficiently motivating driver decisions in real-time while maintaining a positive driver experience for long-term market health. This team is responsible for the efficient reinvestment of significant budgets to optimize supply conditions, providing effective supply controls etc., where and when the marketplace needs it most. Key areas include managing Bonus Zones, Priority Mode, and developing algorithms to improve budget allocation for maximum market throughput.
We are looking for a Data Science Manager to lead data science initiatives for the Real-Time Supply Management (RTSM) team. You will play a pivotal role in developing the vision, setting roadmaps, and leading the execution of data science projects that directly impact Lyft’s marketplace efficiency and driver engagement. You’ll partner closely with product, engineering, and operations leaders to build and scale our real-time incentive systems, shape long-term strategy, and deliver on critical business goals. You will initially be hands-on in building models and pipelines, gradually shifting to more managerial responsibilities as the team grows. The ideal candidate will have strong experience in algorithm development (particularly in optimization, machine learning, or causal inference), thrive in a fast-paced environment, and possess a hands-on, entrepreneurial mindset to drive results..

Responsibilities:

• Lead, mentor, and grow a high-performing team of data scientists with diverse backgrounds, including optimization, experimentation, machine learning and causal inference.
• Develop and deploy machine learning models, algorithms, and systems to optimize real-time supply management, including Bonus Zone budget allocation and Priority Mode effectiveness.
• Define and drive the data science vision, strategy, and roadmap for RTSM, aligning with overall business objectives to improve market throughput and driver experience.
• Provide strong technical guidance and coaching to the team on complex data science problems related to real-time decision-making and resource allocation.
• Champion data-driven decision-making and prioritization within the RTSM team and with cross-functional partners.
• Lead deep-dive analyses into large-scale datasets to identify opportunities for improving incentive efficiency, spend accuracy, and overall market health.
• Collaborate with engineering and product teams to design, implement, and iterate on new features and algorithmic improvements for real-time incentives.
• Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies.

Experience:

• Advanced degree (MS or PhD, PhD preferred) in a quantitative field like Operations Research, Computer Science, Statistics, Engineering, or a related area; or equivalent work experience.
• 5+ years of hands-on technical experience in machine learning, causal inference, optimization, or data science, preferably with applications in real-time systems or marketplace dynamics.
• 1+ years of management experience building, leading, and mentoring data science teams.
• Proven track record of leveraging data science, optimization, and/or machine learning to drive significant business outcomes.
• Experience guiding teams through ambiguous and complex technical challenges to deliver impactful solutions.
• Strong understanding of experimental design and causal inference.
• Excellent communication and collaboration skills, with the ability to articulate complex technical concepts to diverse audiences.
• Hands-on experience with large-scale data processing (e.g., Spark, SQL) and machine learning frameworks is highly desirable.
• Familiarity with real-time bidding, dynamic pricing, or supply chain optimization is a strong plus.

• Lead, mentor, and grow a high-performing team of data scientists with diverse backgrounds, including optimization, experimentation, machine learning and causal inference.
• Develop and deploy machine learning models, algorithms, and systems to optimize real-time supply management, including Bonus Zone budget allocation and Priority Mode effectiveness.
• Define and drive the data science vision, strategy, and roadmap for RTSM, aligning with overall business objectives to improve market throughput and driver experience.
• Provide strong technical guidance and coaching to the team on complex data science problems related to real-time decision-making and resource allocation.
• Champion data-driven decision-making and prioritization within the RTSM team and with cross-functional partners.
• Lead deep-dive analyses into large-scale datasets to identify opportunities for improving incentive efficiency, spend accuracy, and overall market health.
• Collaborate with engineering and product teams to design, implement, and iterate on new features and algorithmic improvements for real-time incentives.
• Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies