Lead Data Scientist, Autonomous Driving

Avride•Austin, TX

3h•Onsite

About The Position

Our team is responsible for understanding how our technology performs and turning that understanding into confident decisions. We bring together simulation data, real-world driving data, structured test results, and human-labeled data to evaluate the stack end to end. We collaborate across the stack and deliver analytics on a wide range of technology aspects, from low-level hardware and performance to behavior quality in specific road scenarios. We answer questions from issues with system performance in specific conditions to how ready we are overall to launch from a safety perspective. We are building the analytics function and looking for people with a leadership mindset and deep analytics experience. You will lead a focus area in evaluation and experimentation, help hire and shape the IC team, guide analysts on scope and methods, and work closely with partner teams. You will integrate simulation and on-road evidence, use a broad toolkit of analytical methods, and produce clear, action-oriented outputs for stakeholders. You are comfortable staying hands-on as an individual contributor.

Requirements

4+ years in analytics for a large-scale or complex technology product.
Strong background in statistical reasoning and methods for evaluating change and uncertainty.
Strong Python and SQL; you can independently run experiments and ship analyses and pipelines end to end.
Proven technical leadership, mentoring, and hiring experience in analytics.

Nice To Haves

Formal training in statistics (for example, MS or PhD in Statistics, Applied Statistics, Biostatistics, Econometrics, or a quantitative field with rigorous statistical coursework).
Familiarity with the modern data stack and experience in data engineering.
Background in autonomy or other technologically intensive domains.

Responsibilities

Understand the technology deeply. Go deep into autonomous driving technology across behavior and hardware.
Evaluate platform health and system behavior, and define the right metrics and metric hierarchy to quantify technology quality.
Contribute to team formation, take part in hiring and leveling up analysts, and set direction for an evaluation focus area while collaborating with technology leaders and with teams that build data infrastructure and analytics tools.
Design, run, and interpret experiments using solid statistical methods. Be ready to analyze results yourself and translate them into well-supported decisions.
Collaborate with data engineers and backend developers to assemble datasets and productionize analysis pipelines that automate recurring computations with attention to performance and reliability.