Senior Data Scientist, AD/ADAS
London, United Kingdom
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Estimated pay
Estimate£82K–£126K/yr
Estimate — the employer hasn't published pay.
Based on 8 similar senior Data Science & ML jobs in United Kingdom that show pay. The range is the middle half of their posted pay (25th to 75th percentile), not an offer — ask the employer for the real range.
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At a glance
- Experience asked
- 3+ years
- Education
- PhD
- Languages
- Japanese
- Industry
- Mobility & Logistics
- Benefits mentioned
- Health insuranceRetirement plan
Skills mentioned
About the role
TEAM
At Woven by Toyota, we tackle Autonomy challenges at the intersection of AI, Robotics, and Advanced Driving. We develop advanced Perception and Planning technologies, and their production software for multiple AD/ADAS systems. To deliver reliable data-driven technologies to millions of Toyota vehicles, we are solving complex real-world problems using large-scale data, machine learning, and state-of-the-art architectures for Perception, Prediction, and Motion Planning.
WHO ARE WE LOOKING FOR?
The team is looking for a strong Data Scientist to drive the creation and deployment of sophisticated data models and algorithms to resolve business challenges for the Autonomy team. You will combine modern data approaches with safety standards while also considering cost efficient options. You will be an expert in deep learning and software engineering. Furthermore, you are proactive towards handling the processes required for production development and approach them by asking "What can I do for you?" with a "Giver" mindset.
RESPONSIBILITIES
Develop data strategies on how to collect, sample, label, handle, and leverage large amounts of data from Toyota’s market fleet, test vehicles, and simulators, to directly improve machine learning model performance across perception and planning stacks
Lead data-related activities across the autonomy team (perception, planning, product evaluation, etc), and across the company in collaboration with the Toyota group, by collaborating closely with stakeholders from multiple teams in different time-zones
Analyze large-scale data and identify its characteristics to propose technical solutions for data curation, model improvement and performance evaluation techniques
Develop evaluation frameworks and metrics to assess planner performance, identifying edge cases and high-value scenarios to improve system behavior.
Design and improve the scalable data pipelines and automation for machine learning and performance evaluation
Enhance the overall team capabilities in data science by initiating and leading initiatives across the Autonomy teams and mentor team members.
MINIMUM QUALIFICATIONS
Masters or PhD degree in a quantitative field related to Science and Engineering.
5+ years experience in following industries or research areas: AD/ADAS, Robotics, Computer Vision, or Behavioral Planning.
3+ years experience in data science or related areas, including theoretical aspects of data science like machine learning (deep learning, statistical analysis, A/B testing, and mathematical modeling)
Experience in building machine learning algorithms and infrastructure, such as: data pre- and post-processing, sampling and curation, ablation studies, evaluation
Experience writing software using: i) Python for data science (numpy, scipy, scikit, pandas; ii) database; and iii) cloud platform services (AWS, GCP, Azure)
NICE TO HAVES
5+ years experience in data science or related areas
2+ years tech lead or management experience in data science or related areas for business applications, such as production, commercial service or public service development
Hands-on experience in the following:
Analyze huge (e.g. peta byte) scale database
Experience with temporal/sequential and/or spatial data
Develop perception-related technologies, such as camera image processing, computer vision, machine learning, or deep learning
Design data annotation rules for machine learning
Build or manage infrastructure, such as Docker, Kubernetes, Jenkins, GitHub Actions
Behavior prediction, imitation learning, reinforcement learning, or end-to-end models.
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