Level

Dojo

Data Scientist

AI in this role

databricks
ml-opsai-evaluation

We’re reinventing payments. In less than four years, Dojo disrupted the market to become the largest and most loved acquirer in the UK. Our payments infrastructure, purpose-built for in-person commerce, is game changing.

 

Now, over 150,000 customers across four countries choose to transact billions with us every year. But we’re just getting started.

 

Our people are the driving force behind our success. They are our greatest investment and our ultimate competitive advantage. We hire exceptional people and give them the autonomy, trust, and ownership to thrive. The results take care of themselves.

The role

 

A Machine Learning Data Scientist designs, builds, and deploys the scalable models and algorithmic systems powering Dojo’s automated decisions—from fraud prevention to operational efficiency. Where off-the-shelf scripts promise easy answers, your job is to build systems that survive production: balancing statistical rigor with clean software engineering to ensure our algorithms are robust, interpretable, and scale flawlessly.

 

On our Builder track, you'll focus on deep algorithmic execution, numerical optimization, and software craft. You're the person the team turns to when a prediction or optimization problem is hard, standard machine learning isn't enough, or a model degrades in the wild. You protect the integrity, reliability, and long-term health of our live production systems.

 

What you will do...

 

  • Own workstreams: Drive technical projects from ideation to production, anticipating domain problems and aligning your roadmap with key commercial levers.
  • Design pragmatic systems: Tackle complex modeling problems and design end-to-end ML systems, prioritizing robustness and maintainability over over-engineering.
  • Formulate optimization solutions: Build numerical optimizers (LP/MIP), tune solvers, and design custom heuristics when exact objective functions or data are scarce.
  • Maintain production rigor: Take responsibility for live models, monitoring for drift, preventing train/serve skew, and debugging subtle mathematical failures.
  • Write production-grade code: Write well-structured, tested Python and SQL at scale, using Databricks and advanced data manipulation techniques.
  • Drive AI workflows: Safely leverage AI tools to boost development productivity, while rigorously verifying output logic and optimization reasoning.

 

What you will bring...

 

  • Mathematical grounding: Strong statistical background to reason about bias/variance and model evaluation, diagnosing subtle failures from first principles.
  • Optimization & heuristics: Experience formulating business constraints into numerical optimization problems and designing custom heuristic algorithms.
  • Python & SQL craft: Mastery of Python, Databricks, and the modern ML ecosystem, combined with writing highly efficient, scalable SQL.
  • Production MLOps: Practical experience maintaining live models, including CI/CD pipelines, reusable feature architectures, and drift detection.
  • Technical honesty: Commitment to evaluating models rigorously using metrics reflecting true business value, and communicating limitations transparently.
  • Collaborative leadership: Track record of owning components end-to-end, cutting through ambiguity, mentoring junior peers, and setting engineering standards.

 

Dojo home and away

 

We believe our best work happens when we collaborate in-person. These “together days” foster communication, drive innovation and spark our brightest ideas.

 

That's why we have an office-first culture. This means working from the office 4+ days per week.

 

With offices across Europe, we know a thing or two about staying dynamic. Need deep focus? Head to a quiet zone. Big ideas? Collaboration spaces have you covered. Just here for a catch-up? Our social hubs make it easy. Do work that counts, in spaces made for you.

 

Question: what’s curious, relentless, and customer obsessed?

 

If you’re keen to know the answer, you’re a third of the way to meeting our Dojo values.

 

If the following speak to you, let’s talk:

 

You’re curious. You have a real desire to learn and create.

 

You’re relentless. You keep going even when it’s easier not to. 

 

You’re customer-obsessed. You know how important customers are to what you do. 

 

Diversity, equity, and inclusion at Dojo

 

From local bakeries to well-known eateries, Dojo payments serve over 150,000 places across the UK.

 

And something that’s fundamental to creating relevant, innovative products at Dojo is to build teams to reflect the diversity of the businesses we serve.

 

Our drive to improve diversity, equity, and inclusion is closely linked to helping employees thrive and innovating for better customer experiences.

 

If you care about your work, you’re curious, and you think customer-first, you have a place at Dojo.

 

To make sure you’re the best you can be throughout the recruitment process, let us know if you need any extra adjustments to help you thrive.

 

Visit dojo.careers to find out more about our benefits and what it’s like to work at Dojo, or check out our LinkedIn and Instagram pages.

 

#LI-Hybrid

How we rate this

Data Scientist at Dojo rates 4 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

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

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsAI EvaluationDatabricks

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. How do you decide that one model's output is better than another's for a given task?
  3. What are the limits of Databricks that you've run into, and how did you work around them?

Adapt your resume

  • List these exact terms on your resume: ML Ops, AI Evaluation, and Databricks. 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.

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