Machine Learning Engineer 1211 views

About Monzo

At Monzo, they’re building a bank that is fair, transparent and a delight to use. They’re growing extremely fast and have over 3.5 million customers in the UK, with over 250,000 new people joining every month. They’ve built a product that people love and more than 80% of our growth comes from word of mouth and referrals.

They are looking for people who are passionate about changing the way the industry works and who share their values. They believe in an open, transparent, highly diverse working environment where everyone is empowered to make changes. Following Stripe’s example, all emails in the company are available for everyone to explore, and they discuss everything in Slack channels which are accessible by everyone in the company. You can see more about their plans on their public product roadmap, and sneak peeks of upcoming features in their community forum.

The data team

The data team’s mission is to

Enable Monzo to Make Better Decisions, Faster

This mission encompasses three major areas of work: (1) product analytics, to help teams understand their customers and improve their app (2) domain analytics, to support teams who are working in specific banking disciplines (e.g., lending, finance, and financial crime), and (3) machine learning, where they design and build system that automates decisions across Monzo. While they take a flexible approach and frequently help each other across these areas, they each have one domain that is their primary focus.

For this role, they are looking for an Engineer who will focus on machine learning. The machine learning squad partners with teams across all of Monzo to design, build, and experiment with machine learning systems that help them achieve their goals. For example, see their recent work with related articles in the app help screen, and the approach they took when building the help screen’s search algorithm. This squad is currently two Data Scientists and one backend Engineer.

The role

You’ll be joining the squad to focus on production machine learning, working with many teams across Monzo to design, build, analyze, and experiment with machine learning systems that make use of the data we gather.

They organize their machine learning projects into three distinct phases; you’ll spend your time doing all three:

• • Explore. They use BigQuery and Jupyter Notebooks to analyze data and design machine learning models for offline evaluation. For example, they are investigating machine learning-powered assistants for their customer operations team, classifiers to detect financial fraud, and NLP models to improve the app’s help screen.
  • Launch. They build Python microservices and cron jobs to put promising machine learning models into production. They are actively working on automating as much of this step as possible: their goal is for any Data Scientist to be able to deploy a promising new model to production in less than a day.
  • Iterate. They run A/B tests in partnership with other teams and analyze the results. Based on outcomes, they may decide to roll the model out to every customer or to explore improvements to the model for further testing.

What’s special about data & machine learning at Monzo?

Autonomy. They believe that people reach their full potential when you can remove all the operational obstacles out of their way and let them run with their ideas. This comes together with a strong sense of ownership for your projects. At Monzo, you will get full access to our data and analytics infrastructure. When you discover something interesting, there is nothing stopping you from exploring and implementing your coolest ideas.

Cutting-edge managed infrastructure. All their data infrastructure lives on the Google Cloud Platform, so you don’t need to spend your time configuring or managing clusters, databases, etc. If you want to train a Machine Learning model faster, just spin up a compute engine instances and submit a job from your local machine, no DevOps skills required.

Automation. They aim to automate as much as they can so that every person in the team can focus on the things that humans do best. As with all data science work, there’s some analysis and reporting, and as much as possible they encourage self-serve access to their data through Looker.

You Should Apply If

• • What they’re doing sounds exciting, and you can’t wait to explore their data
  • You’re impact driven and eager to have a real positive impact on the company, product, users and very importantly your colleagues as well
  • You have a self-starter mindset; you proactively identify issues and opportunities and tackle them without being told to do so
  • You’re a team player whom your colleagues can rely on
  • You have a solid grounding in SQL and Python and are comfortable using them every day
  • You’re happiest exploring data, designing and evaluating machine learning models and seeing these projects all the way through to production
  • You’re excited about the potential of machine learning and can communicate those ideas to colleagues who are not familiar with the domain
  • You’re adaptable, curious and enjoy learning new technologies and ideas

Logistics

We can help you relocate to London and we can sponsor visas.

Their interview process typically consists of a 30-minute initial phone screen, a take-home test, and a half-day on-site interview. They promise not to ask you any brain teasers or trick questions.