Data Science Manager, Mapping

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.

Our transport network serves the needs of millions of people every day who want to get from one place to another using Lyft cars, bikes and scooters, with public transportation, or on foot in the most efficient way. To serve these needs, we need to suggest the fastest, most affordable and safest routes. We achieve this by processing millions of rides, taking into account the latest traffic information and analyzing the preferences of drivers.

To strengthen our efforts, we are hiring a Data Science Manager who will lead data scientists and data analysts helping us to make data driven decisions. Data Science & Analytics is at the heart of Lyft’s products and decision-making. You will leverage data and rigorous, analytical thinking to shape our mapping products and make business decisions that put our customers first. This will involve identifying and scoping opportunities, shaping priorities, recommending technical solutions, designing experiments, measuring the impact of new features and monitoring our solutions in close collaboration with many engineering teams. You will help us solve some of the most impactful problems in mapping, including:

• How do we provide the best routes and most accurate ETAs?
• How is the routing experience for our drivers? Are we providing the fastest, most economic and most comfortable routes to our customers?
• How do we benchmark and measure the success of map services?

Our technology stack is based on the latest technologies such as AWS, Kubernetes and Apache Airflow. You will work with incredibly passionate and talented colleagues from software engineering, machine learning and data science on projects that directly impact millions of riders and drivers.

Responsibilities

• Lead and grow a high-performing team of data scientists with diverse backgrounds, including optimization, experimentation, machine learning and causal inference  
• Define and drive the data science vision, strategy, and roadmap, aligning with overall business and product objectives to improve market competitiveness and user 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 by partnering with product managers, engineers, marketers, and leaders to translate data insights into decisions and action
• Lead deep-dive analyses into large-scale datasets to identify opportunities for improving navigation efficiency, mapping accuracy, and overall product health
• Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies
• Mentor and guide the professional and technical development of your team members. Help develop their careers, and assign them to projects tailored to their skill levels, personalities, work styles, and professional goals
• Maintain a balance between building sustainable, high-impact projects and shipping things quickly
• Work closely with the Lyft recruiting team to hire high potential candidates from diverse backgrounds

Experiences

• 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
• 2+ years of management experience building, leading, and mentoring data science teams
• Experience launching and monitoring consumer facing products and iterating through data-driven experimentation and metrics analysis
• Experience guiding teams through ambiguous and complex technical challenges to deliver impactful solutions
• Hands-on experience building or operationalizing machine learning models (e.g., propensity, segmentation, churn, personalization) in partnership with engineering or platform teams
• Strong expertise in statistics, experimental design, and causal inference, including A/B testing, multivariate testing, and incremental lift measurement
• Strong data storytelling and influence skills, with experience pre