The basics

Your title will be Data Analyst
This role pays £50k-£80k/year + equity
Matt, Cofounder and CTO, is the hiring manager
We work Mon-Thu in our office in Chancery Lane, London

What are we building? An AI Executive Assistant

In 1930, the economist John Maynard Keynes predicted that we'd only be working 15 hours a week by 2030. Despite automation in agriculture and industry, that hasn't happened.

Why? The service sector. Walk around the average office and you'll see people's days taken up by emails, Slack and meetings instead of real work.

People in client facing roles - sales, professional services firms, recruiters - feel this most acutely. Instead of advising clients, they spend hours on admin. Following up. Scheduling meetings and taking notes on them. Answering repetitive questions over email.

We've built an AI executive assistant that looks at your emails, messages and meetings, and uses that knowledge to answer your email, schedule meetings, take next steps from meetings and organise your inbox.

Unlike other startups, we're a pure-play applied AI company, not a SaaS company with AI features hastily bolted on! We make use of the best techniques (fine tuned open source models, tool use, and retrieval augmented generation) and as a result, users send 53% of the email drafts we generate.

How has it been going?

Since launching in March 2024, we've gone from $0 to $2.5m in revenue across 3000 paid users. We did this with a team of 4, and without using any paid marketing until November.

We spent Q4 2024 in San Francisco at HF0, the best AI startup accelerator in the world, learning from experts like the CEO of Instacart, Head of Applied Research at OpenAI and the cofounder of Snowflake.

What do we value?

We're very intentional about adding new people. We think a small team of exceptional people working hard at a problem they care about will always beat a larger, more unfocused team. That means you'll need to bring an intensity to this role that might not be asked at other companies. But it also means you will be fast tracked into more senior roles and responsibilities far earlier.

We also believe in hiring people who want ownership and autonomy in their work, and giving it to them. Instead of just being handed tickets, you'll be proactively suggesting metrics that should be monitored and one off analyses that should be done, and proposing improvements to our analytics infrastructure.

What will I do?

Running analyses in SQL to make decisions on product, marketing spend, and pricing strategy, and communicating your findings, method, and recommendations in a clear way to both technical and non-technical audiences
Working with stakeholders in the marketing, sales, customer success and product departments to agree metrics that need to be monitored on an ongoing basis, then building visualisations
Working with data engineers to ensure data is modelled in the correct format to support data visualisation and more detailed analyses

What does our ideal hire look like?

You've worked at an early stage tech company (<100 people) as a data analyst or similarYou have an expert level understanding of both SQL and a data visualization tool (we use Metabase). If you don't know SQL to a very high standard, you shouldn't applyYou are excellent at communicating your analyses to both technical and non-technical audiences: the results, your recommendations, and the method you usedYou can proactively structure an analysis given only a business problemUrgency and intensity in your work

Our tech stack

Broadly, we use a fairly typical ELT stack. It's not a requirement to have worked with every tool we use, but the more the better!

BigQuery as our data warehouse
Metabase for data visualization
Fivetran to pipe raw data from third party tools (eg Stripe, which we use for billing) into our data warehouse
dbt hosted on Github Actions for transformation of raw data into production tables ready for consumption
Census for reverse ETL

The application process

Submit your CV (no need for a cover letter)
An initial call with someone from the Fyxer AI team to review your experience and motivation for joining (15 mins)
Live coding exercise - SQL (30 minutes)
Analytical case study (45 minutes)
Meet Richard - CEO (30 minutes)

Fyxer AI

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