Meet our data team
Our data team is task with applying expertise in data science, data analysis, and AI engineering to understand how our users behave. Their models and insights feed into data products as well as into our wider product strategy.
They do things like:
- Build agentic workflows that automate reasoning, data analysis, and decision-making across the business.
- Integrate large language models and retrieval systems into data products to deliver contextual, dynamic insights in real time.
- Play with transaction data to recognise temporal or spatial patterns.
- Build risk insights by combining onboarding data with in-life behaviour to detect anomalies and automate interventions.
- Design dynamic pricing models that personalise payment pricing based on business usage, risk, and segment.
- Launch product recommendation logic that serves merchants contextual bundles at the right moment in their lifecycle.
- Benchmark provider performance to show which of our partners are outperforming across time to board, churn, and margin.
- Create intelligent and personalised user experiences based on customer context to increase business-product fit and reduce time to conversion.
By the end of your first sprint, you will likely have built at least one data project live in production, in use by some of the UK’s largest banks and thousands of their small business customers.
The Stack
Our data stack is modern and evolving. You'll be responsible for shaping and maturing the architecture to fit our stage and scale, as long as it sits within AWS.
Our current data platform includes:
- Python for data modelling and analysis
- Agentic frameworks such as LangChain and LlamaIndex
- Airbyte to load data from external sources
- RDS Postgres for storing product data
- Amazon Redshift as our data warehouse
- DBT for data modelling, transformation, and testing
- Metabase for internal reporting, deep dives, and data exports
In case you're interested, our software stack includes React, TypeScript, GraphQL, REST, Express, Relay, Prisma, Redis, Postgres, AWS, Styled Components, and Storybook.
We like to experiment with different technologies and libraries, particularly when building proof of concepts. Make your case, and we’ll consider expanding our tech stack.
You're a great fit if
- You have a solid knowledge of data science and AI engineering.
- You work end-to-end and can design, engineer and maintain our data stack.
- You've worked with agentic frameworks (LangChain, LlamaIndex) in the past.
- You’ve had exposure to a previous high-performance environment - this may be another startup backed by tier 1 investors, but it could just as easily be a competitive hobby.
- You know what good engineering practices look like - we need you to work with our engineering team to integrate your insights into our products.
- You find the extensive breadth and ambiguous nature of this role inspiring.
You're a bad fit if
- You lack genuine interest in data - we want you to be obsessed.
- You’re unopinionated on what you work on - we want you to care.
- You’re not interested in the commercial context or wider business strategy.
Our ways of working
At Tuza, we've founded our working principles with influences from Basecamp’s product development framework, Shape Up, Matt Lerner's Growth Levers and How to Find Them, and Melissa Perri's Escaping The Build Trap.
The crux is that we like to focus on the ‘why’. Why are we building this product, or feature, and what outcome are we hoping for?
We value focusing on our customers, output over input, and experiment more than we optimise. We outline how we work in more detail here. This isn't static: we're constantly aiming to improve based on team feedback.
What's in it for you
- A competitive salary with generous equity.
- A hybrid working approach. We're based out of our Shoreditch office.
- Flexible working hours.
- Unlimited holiday with a minimum of 24 days.
- Smart and kind teammates - leave your ego at the door.
- The occasional soundbath…
