Senior Data Scientist - AD/ADAS

Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society.
Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.

TEAM

The cloud and data engineering team accelerates autonomous driving by providing access to the data collected by our fleet of autonomous and non-autonomous vehicles, and provides the core technology to mine interesting and rare events out of the petabytes of data we collect.  Efficient, targeted and cost-effective access to data at scale is key to tackle the hardest problems in AD/ADAS, from developing the Machine Learning (ML) models for perception and prediction of human driving patterns, to increasing the sophistication of our validation and simulation by identifying rare and interesting real-world driving situations.  We are a distributed team, working in the UK, US and JP.

WHO ARE WE LOOKING FOR?

The Cloud and Data Engineering team is looking for data science engineers who are passionate about enabling the next generation of automotive software development. Our data science engineers employ statistical modelling and measurement frameworks to model the distribution of road events in the real world, and inform our long-term validation and ML training data strategy. They embed within our engineering teams to help solve domain specific data challenges, such as developing evaluation frameworks for AD/ADAS deployment readiness and the fidelity of simulation. The right candidate will have excellent communication skills, experience in using statistical methods in an applied setting and in developing metrics and evaluation frameworks, as well as familiarity with Machine Learning systems.