Data Analyst, Product

Apron was started by a group of people who’d spent years building products for global fintech companies. There was one big problem no one was solving: business payments. The kind that buy tomatoes, tools, and till rolls. The kind that keep suppliers happy and business booming. The kind that should be super simple to make and manage, and yet, aren’t. Payments eat up valuable hours every week for both businesses and the accountants and bookkeepers who help them.

This is a problem that’s affecting entrepreneurs. Florists and financial analysts. Brewers and brand strategists. The kind of people who build things, break things, change things. Imagine what they could do with this time instead. What would they come up with? What would they create?

That’s why we built Apron as a payments powerhouse. We flip the payment experience from blocking business to boosting it. Apron pulls all things payments together – weaving into your workflow, collating conversations, turning hours into minutes. So you can put those hours to better use – plan the future, take a walk, call your mum.

We are backed by Index Ventures and Bessemer Venture Partners.

We are building a product that allows our clients to upload all invoices and receipts and get them automatically processed and ready to be paid. We are building our own document recognition system that works fast, is reliable and really cost efficient.

We are seeking a Data Analyst who will help us clearly understand our current performance and guide the direction of our product development through data and metrics. We need someone who is eager to dive into both the product and technical aspects of our AI document recognition system.

You should have a passion for communicating your findings and engaging others with your results, fostering a data-driven culture. You will be expected to propose ideas for research and gather hypotheses from team members.

We value close contact with our users, so a willingness to validate research questions and results with our users is a significant plus.

You will play a key role in developing our data function, recommending tools and methodologies to integrate into our workflow. This role is hybrid, to be based in our London HQ (Liverpool Street/Shoreditch High St) a minimum of 3 days per week.

• Developing a system of metrics and dashboards for online monitoring of our system. These will enable us to track product growth, monitor the quality of document recognition, and respond promptly to any issues that arise.
• Proposing improvements to the recognition system based on data analysis. For example, analysing user corrections to identify which documents and fields need improvement first.
• Providing data for product development like investigating the need for new UI features, and backlog prioritisation. You will analyse user behaviour to find insights for product enhancement.
• Performing ad-hoc analysis of problems encountered by users or anomalous behaviour of the recognition system.
• Organising the process of conducting and evaluating the results of A/B tests.

• Proficiency in data analysis tools such as SQL and Python: experience writing efficient queries for PostgreSQL. Some tasks will require running tests on our internal datasets or making automation. Basic knowledge of Python and Pandas is expected.
• Experience with data visualisation tools like Tableau, Grafana, or similar platforms.
• 3+ years experience in an analytics role. Experience in product analytics is required.
• Strong knowledge in statistics and probability, along with proficiency in designing and interpreting A/B tests to support data-driven decisions.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills to present findings clearly to both technical and non-technical stakeholders.
• Understanding of basic ML concepts is a plus.

• 29 days Annual Leave (exclusive of public holidays)
• Birthday day off (if it falls on a weekday)
• Weekly Deliveroo budget
• AXA Healthcare Insurance (with Dental and Optical Cover)
• Stock Options
• Fully expensed tech