Data Scientist Manager, Engineering

Job description

Job responsibilities

• 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.

Job requirements

• 8+ years of experience in data analytics, data science, or operational analysis, including 3+ years of experience directly managing and growing analytical teams.
• B.S or M.S degree or equivalent practical experience in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.
• Proven experience analyzing complex hardware/software systems, robotics, or massive simulation environments.
• Proven ability to design and implement robust data pipelines, dashboards, and automated alerting systems from the ground up.
• Expertise in applied statistics, experiment design, and A/B testing, with a strong track record of using these methodologies to quantify system performance.
• Strong programming and data querying skills using Python, SQL, or R to manipulate massive datasets and extract actionable insights.
• Exceptional ability to translate complex data into clear narratives, presenting analytical findings effectively to engineering partners and organizational leadership.