Level

Zego

Lead Data Scientist

Zego is hiring a Lead Data Scientist for a remote role open to applicants in United Kingdom. Level rates it ; you can apply on Zego.

Level's read on this job

  • On Level's scale, this data scientist role is Builds AI โ—โ—โ—โ—: the job is building AI systems.
  • This is listed as a remote role.
  • The posting mentions scikit-learn and MLflow among its tools and skills.
  • Level classes it as a lead role.
  • Zego has 40 other open roles on Level.

AI in this role

scikit-learnmlflow

About Zego ๐Ÿš€

At Zego, we're on a mission to do the good thing, not the insurance thing.

Insurance hasn't changed much in over a century. The way we live, work and travel has. We're building the real-time, AI-driven infrastructure that powers innovative insurance, so good drivers get cover that works the way they actually drive.

We're not just updating insurance; We're leading the AI evolution in insurance ๐Ÿค–

For us, AI isn't a line on a roadmap or a buzzword on a slide. It's our operating reality, and it's how we build products that back drivers instead of the old insurance playbook.

We don't do things slowly, and we don't do bureaucracy. We back high-performance builders who want ownership, early responsibility and the chance to do the most career-defining work of their lives. You'll get the space to try things, the tools to move fast, and the room to see your ideas reach millions of drivers.

Do not take our word for it. Read what Zegons say about us on Glassdoor.

If you're ready to build the future of insurance, we're hiring.

The team

The Telematics team turns raw phone sensor data into meaningful signals about how people drive. Using signal processing and machine learning on high-frequency GPS, accelerometer, and gyroscope data, we extract the behavioural features that power Zego's understanding of driving quality, context, and risk.

The role

Few teams use phone sensor data to price commercial motor insurance at scale. As a Lead Data Scientist, you'll shape how millions of trips are turned into risk signals. You'll own behavioural features and algorithms from hypothesis to production, working at the intersection of data science and engineering on GPS, inertial, and other sensor data.

You'll move between quick heuristics and full ML models depending on what the problem calls for. You ship code to production, not just notebooks!

What you will be doing

  • Design and build the behavioural features and factors that drive Zego's understanding of driving quality, context and risk

  • Turn raw, high-frequency GPS, accelerometer and gyroscope data into usable signal at a scale of more than 250,000 trips a day

  • Research new behavioural features and detection algorithms, reading the literature, testing ideas and killing the ones that do not survive contact with real data

  • Take work from hypothesis to production, prototyping in notebooks then writing the production-grade Python and SQL that scales

  • Choose the simplest solution that holds, rule-based when that is enough and machine learning when it is warranted

  • Lead experiments, validate impact with data, and automate insight generation

  • Get features into the pricing path alongside engineers, product managers and actuaries, and translate what the sensor data shows into findings non-technical colleagues can act on

What you will need to be successful

  • Quantitative foundations. An MSc, or integrated MEng or MSc, in engineering, computer science, physics, mathematics or a similar field. This is a core role and it needs strong engineering foundations

  • Signal processing instinct. Working knowledge of digital signal processing or sensor physics. You can reason about noise, sampling, filtering and the physical meaning behind a signal, not just its numbers

  • Production-grade delivery. A proven track record taking data science or data engineering projects into production, with strong Python and SQL (we use Snowflake) and the scientific Python stack: Polars, Pandas, NumPy, SciPy and scikit-learn. You have designed, built and maintained reliable, observable pipelines from scratch

  • Machine learning judgement. Experience developing and evaluating ML models on tabular data, classification or regression, where the evaluation matters as much as the model choice

  • Clear communication. You turn ambiguous problems into structured, testable ideas and share insights clearly with technical and non-technical audiences

  • You work AI-first. You will use AI daily here, and we mean daily. You do not need to arrive an expert, but you do need to arrive curious, experiment fast, and take ownership of getting good quickly. People who wait to be trained will find this uncomfortable

Nice to haves

  • Experience applying signal processing to noisy, high-frequency sensor/time-series data (GPS, accelerometer, IMU) in production

  • Exposure to insurance, mobile data, or behaviour modelling

  • Experience with cloud platforms (AWS), containerisation (Docker, Kubernetes), or data orchestration frameworks

  • Familiarity with our ML and tooling stack: MLflow for experiment tracking, DVC for data versioning, Streamlit for internal apps and demos

What it's like to work at Zego

At Zego, we operate like a high-performing sports team. We come together as one, driven by ambition, backed by real support, and united by a shared mission to win.

Working here means having the backing, the coaching, and an inclusive community of Zegons across the UK and Europe who celebrate your wins and raise your game. It also means holding ourselves to high standards: moving with urgency, taking complete ownership, and embracing honest feedback that helps us continuously learn and evolve.

This is your opportunity for a career-defining experience, and one you will own.

The Zego ways of working ๐Ÿก

Teams work better with time to collaborate and space to get things done. We call it Zego Hybrid: some of us are in our office (central London or Halifax) weekly, others monthly or quarterly. It's about finding the balance between face time and focus that produces great work and a healthy life around it.

We also make a point of getting everyone in the same room. Teams come together every quarter, and once a year the whole company does, properly. It's a serious investment and consistently one of the best parts of the year.

Here is what the last one looked like.

Benefits ๐ŸŽ

๐Ÿ’ฐ Market-competitive salary, benchmarked against your function and reviewed every year.

๐Ÿ† Annual performance bonus, linked to company performance and your contribution.

๐Ÿ“ˆ Share options, a real stake in the company and a share in the future you build

๐Ÿฉบ Private medical insurance.

๐Ÿ–๏ธ Pension, generous holiday, and ยฃ1,000 a year to spend on getting to the office or learning something new.

๐Ÿ’ป Cutting-edge systems and tools, so you always have what you need to drive your impact.

Ready to do the most impactful work of your career? We want to hear from you.

Zego, leading the AI evolution in insurance.

Equal opportunities ๐ŸŒˆ

We're an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, or disability status.

How we rate this

Lead Data Scientist at Zego rates 83 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. โ—โ—โ—โ— Builds AI80 to 100
  2. โ—โ—โ—โ—‹ Works on AI60 to 79
  3. โ—โ—โ—‹โ—‹ Uses AI40 to 59
  4. โ—โ—‹โ—‹โ—‹ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

scikit-learnMLflow

Also common in data scientist jobs at this level

Pay

Typical data scientist job
$166k

This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs

Questions you could be asked

  1. What's a project where you used scikit-learn hands-on?
  2. Walk me through how you've used MLflow in your day-to-day work.
  3. How would you decide a model or AI system is ready to ship?
  4. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: scikit-learn and MLflow. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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