Wayve
Data Science - Manager

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About us
Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.
Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.
In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.
At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.
Make Wayve the experience that defines your career!
The role
As the manager of our applied methods Data Science team, based in the UK, you will lead a group of data scientists who act as trusted partners to the Autonomy org. Your team turns experimental and observational analysis of real and simulated driving into the evals and insights that let engineering teams hill-climb rapidly towards our customer commitments. Your remit is to broaden that team's impact, speed up its delivery, and scale it through both additional headcount and technology, helping to advance Wayve as the leader in end-to-end AI for autonomous mobility.
Key responsibilities:
- Set and align priorities with stakeholders across Autonomy, and maintain provisional 3/6/12 month roadmaps that balance customer demand against team capacity
- Work with your technical leads and senior engineers to review technical proposals and designs, holding a high bar for statistical rigour and eval fidelity
- Monitor status and delivery cadence, surface schedule slippage early, and manage the trade-offs with customers
- Grow the team: hire and onboard new data scientists, and invest in the tooling and automation that let the team scale beyond headcount alone
- Guide career growth conversations with your engineers, matching business needs to development opportunities and building out progression pathways
- Build the cross-functional relationships that keep the team close to its customers and ahead of where support will be needed
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
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Why you're a good match
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Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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About you
Essential
- 3+ years experience managing a team of 5+ ICs
- Track record of growing and maintaining high-performing teams
- A strong data science foundation of your own, including experience applying causal inference methods, whether experimental or observational
- Track record of building strong working relationships with cross-functional stakeholders and customers, and of turning their needs into team priorities
- Experience setting technical direction and reviewing the designs and proposals of senior engineers
- Comfortable planning and communicating roadmaps, and managing delivery and expectations with demanding customers
- Comfortable working asynchronously across time zones with cross-functional partners
- You are deeply curious about building something new and relish the idea of helping to define AV2.0 and how we build it
Desirable
- Experience building or scaling an applied or embedded data science function, including the technology and tooling that lets a team scale beyond headcount
- Familiarity with simulation, offline evaluation, or measurement for ML systems, ideally generalising across vehicle platforms and sensor stacks
- Practical experience with machine learning (e.g. pytorch), and a track record of taking research ideas to production
- Track record of promoting statistical rigour and experimental best practice across a team
- Prior experience with large datasets and distributed computing (e.g. spark, hadoop or other map-reduce tech)
- Experience working in a fast-moving tech company or startup


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This is a full-time role based in our office in London (2 days a week in the office). At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.
Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.
We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.
At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.
For more information visit Careers at Wayve.
To learn more about what drives us, visit Values at Wayve
For US candidates only, please visit E-Verify Notice and Participation and Right to Work
DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.
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