Data Scientist / ML Engineer

About Us

We're a rapidly growing maritime tech startup passionate about technology and innovation. Last year we raised a $4.8M seed round from leading venture investors in maritime, supply chain and insurance. We've gained strong early traction including partnerships with major ship owners and are building serious momentum. We believe technology can and does make the world a better place, and we believe we can make that happen. We value challenging each other, truth seeking and creative problem solving. We celebrate the wins and always have fun.

What we're doing

We're reshaping maritime. It's an industry that's incredibly important and serves as the backbone of global trade, moving $14 trillion of cargo each year. Despite its significance, the industry remains one of the final frontiers of digitalisation.

Ceto provides the operational intelligence layer that technical teams rely on for mission critical decisions making. By capturing and analysing high frequency data from commercial ships, Ceto monitors the condition of the machinery onboard. Through anomaly detection and degradation forecasting we are able to predict mechanical failures ahead of time and guide decision making for our customers. Our analytics are bundled up into insights which are served to our customers through our dashboard, preventing costly breakdowns, loss of hire events and reducing operational risk.

The role

You'll build on our existing products and create new features for current and future customers, working across the full data science pipeline from data cleaning through to feature engineering, model training and deployment. You'll also be expected to offer broader engineering support, spotting and shipping improvements across our analytics stack wherever they're needed.

Your responsibilities will include:

Exploring new ideas in predictive maintenance and condition monitoring, and turning them into reliable, production-ready features

Building and maintaining data science pipelines end to end: cleaning, feature engineering, model training and inference, and output storage

Investigating and resolving the nuances that come with new vessel types, sensors and tags as our customer base grows

Working with time-series data and models, and applying both supervised and unsupervised techniques where they add genuine value

Maintaining high standards of engineering practice: version control, testing, CI/CD and experiment tracking

Working closely with the rest of the technical team to prioritise what's worth investigating and what isn't

Using AI tools such as Claude Code to work faster and more effectively

You'll be perfect for this role if:

You have a physics or physics-based degree, and a genuine interest in the physical systems behind the data, not just the data itself

You've delivered real ML projects into production, not just in notebooks, with exposure to CI/CD and DevOps practices

You have solid experience with supervised ML models and can explain clearly why you'd choose one approach over another

You've worked at a startup, or started your own, and are comfortable with the pace and ambiguity that comes with it

You've built production data science pipelines before, from raw data through to a deployed, monitored output

You have an innate ability to match models to physical problems, and know when a model is telling you something real versus something spurious

You have brilliant problem-framing intuition and a strong desire to build something new

You're a team player who is confident enough to challenge bad ideas and put forward your own, but self-aware enough to know when to do so

You're sceptical by nature. Models can be wrong, intuition can be disproven, and correlations in data often have mundane explanations. You want to understand why, not just what

You have a sense of the big picture and can judge which problems are worth your time and which aren't

It's also a bonus if you have:

Experience in predictive maintenance or predictive analytics

Experience with cloud platforms, ideally Azure, or otherwise AWS or GCP

Experience with MongoDB or other NoSQL databases

Experience with unsupervised methods such as clustering or hidden Markov models

Experience with ML lifecycle tooling such as MLflow

Experience with anomaly detection

A background around boats, cars, bikes or planes

We're less interested in ticking off niche experience like maritime or MongoDB specifically, and far more interested in someone who has worked across different technical stacks and can pick up new ones quickly. A strong generalist who's genuinely curious about the wider engineering problem, not just the modelling, will do better here than a narrow specialist.

What's on offer

Competitive salary based on experience

Equity options

25 days holiday plus public holidays

Birthdays off

Bupa medical insurance

Medicash

Cycle to work scheme

Hybrid working from our London office

Home office stipend to help maximise home productivity

£500 work from anywhere flight allowance

Budget and time allocation for conferences and professional development

The opportunity to shape the future of maritime technology

Join us if you want to be part of a team making a real dent in this massive industry.