Data Science - Manager

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

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

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

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.

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.