Data Scientist Manager, Engineering

About the role

About Wing:

Wing offers drone delivery as a safe, fast, and sustainable solution for last mile logistics. Consumer appetites for on-demand services are increasing, but current delivery methods are inefficient, costly, and contribute to road accidents and air pollution. Wing’s fleet of highly automated delivery drones can transport small packages directly from businesses to homes on-demand, in minutes. We design, build, and operate our aircraft, and offer drone delivery services on two continents. Our technology is designed to be easy to integrate into existing delivery and logistics networks, offering a scalable drone delivery solution for a broad range of businesses. Wing is a part of Google's parent company, Alphabet, and our mission is to create the preferred means of delivery for the planet. If you're ready to do the greatest work of your life, come join us.

About the Role:

Wing is looking for a Data Scientist Manager, Engineering to join our SimEval team. This role is hybrid based in Palo Alto, CA.

As Wing scales its autonomous drone delivery fleet, the volume of simulation and real-world operational flight data is growing exponentially. To accelerate R&D, while maintaining our ability to operate reliability at scale, we are looking for a Data Scientist Manager, Engineering to join the SimEval team. This role will operate at the intersection of engineering, simulation, and analytics, bridging the gap between operational data and our technical roadmap.

In this role, you will lead the System Evaluation team that connects operations and engineering, transforming real-world data into actionable insights that inform engineering direction and ensure scalable and reliable systems.

What You’ll Do:

- Lead, coach, and grow a team of technical data analysts responsible for distilling complex data into actionable recommendations that inform engineering direction and ensure scalable, reliable systems.

- Design and deliver the evaluation workflows, automated triage strategies, and analysis algorithms that aggregate engineering metrics into actionable feedback for engineering leadership.

- Define the metrics, acceptance criteria, and success thresholds that govern our continuous software releases.

- Develop new statistical methods and data pipelines to quantify performance and identify behavioral regressions across our operational domains.

- Connect real-world operational findings directly to the engineering roadmap, and leverage simulation to predict performance of the system with new features and releases.

- Partner closely with engineering teams, flight operations and the Analytics team to turn business metrics into clear insights that drive the technical roadmap and root-cause analysis of system degradation.

What You’ll Need:

- 8+ years of experience in data analytics, data science, or operational analysis, including 3+ years of experience directly managing and growing analytical teams.