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.
Requirements
- 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 presenting insights and recommendations to senior leaders.
- 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.
- Prior experience in mapping domain will be a plus.
Salary Range (San Francisco): $176,000 - $220,000 base pay (not inclusive of equity, bonus, or benefits).