Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Data Scientist, AD/ADAS

Woven by Toyota
London
5 days ago
Create job alert

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 scientists 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.

RESPONSIBILITIES

    • Use statistical modeling to shape the our data strategy for data acquisition (real and synthetic), validation and ML training
    • Develop metrics and frameworks to understand the distribution and diversity of data
    • Tackle ambiguous problems using data-driven analysis, and provide actionable insights to inform decision making and demonstrate business impact
    • Communicate findings on complex technical topics to stakeholders across engineering leadership and product
    • Embed with engineering teams to understand and provide data-driven recommendations on their domain-specific challenges
    • Drive the adoption of best practices in data science across the organisation, lead other data science engineers

MINIMUM QUALIFICATIONS

    • Industry experience using Python for data science (e.g. numpy, scipy, scikit, pandas, etc.) and SQL or other languages for relational databases.
    • Experience with a cloud platform such as (AWS, GCP, Azure etc.)
    • Experience with common data science tools; statistical analysis, mathematical modelling etc.
    • Experience in developing analytical frameworks to facilitate data-driven decision making in the face of high ambiguity
    • A track record of building relationships with engineering and product leadership, and influencing strategic business decisions
    • Ability to communicate concepts clearly and precisely to technical and non technical stakeholders
    • Experience working in a cross-functional environment

NICE TO HAVES

    • Experience in working in a production ML environment
    • Experience working with geographically distributed teams
    • Previous experience in the AD/ADAS domain or adjacent fields (e.g. robotics)
    • Experience with temporal data and/or robotics sensor data.

WHAT WE OFFER

We are committed to creating a modern work environment that supports our employees and their loved ones. We offer many options of the best programs to allow you to do your most meaningful work and to help you shape the future of mobility.

Excellent health, wellness, dental and vision coverage

A rewarding pension

Flexible vacation policy

Family planning and care benefits

Our Commitment

• We are an equal opportunity employer and value diversity.

• Any information we receive from you will be used only in the hiring and onboarding process. Please see our privacy notice for more details.
#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist - Consumer Behaviour - exciting ‘scale up’ proposition

Senior Data Scientist - Consumer Behaviour - exciting ‘scale up’ proposition

Senior Data Scientist

Data Scientist or AI/ML Engineer

Senior Manager Data Science & Analytics

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

Why the UK Could Be the World’s Next Data Science Jobs Hub

Data science is arguably the most transformative technological field of the 21st century. From powering artificial intelligence algorithms to enabling complex business decisions, data science is essential across sectors. As organisations leverage data more rapidly—from retailers predicting customer behaviour to health providers diagnosing conditions—demand for proficiency in data science continues to surge. The United Kingdom is particularly well-positioned to become a global data science jobs hub. With world-class universities, a strong tech sector, growing AI infrastructure, and supportive policy environments, the UK is poised for growth. This article delves into why the UK could emerge as a leading destination for data science careers, explores the job market’s current state, outlines future opportunities, highlights challenges, and charts what must happen to realise this vision.