Senior Decision Scientist

📍London or Remote (UK)  | 💰 Competitive +  Benefits | Decision Science, Borrowing 

About us: 

At Monzo we believe there’s a better way to do banking - we care deeply about our customers, are innovative and execute quickly.

We’re looking to revolutionise the way people manage their finances by putting them in control and promoting their financial wellbeing. We are expanding our range of borrowing products, aiming to create a genuine feel-good factor when it comes to personal credit. We are looking for driven, analytical and creative individuals to help us achieve this goal.

Our technology stack

We rely heavily on the following tools and technologies (note we do not expect applicants to have prior experience of all them):

• Google Cloud Platform for all of our analytics infrastructure
• dbt and BigQuery SQL for our data modelling and warehousing
• Python for data science
• Go to write our application code
• AWS for most of our backend infrastructure

Your day-to-day   

• Develop statistical and machine learning models for our range of Borrowing products, with focus on credit decisions
• Own the full lifecycle of modelling projects, from model design and data curation, to deployment and monitoring in production
• Drive innovation with new data sources and solutions such as Open Banking

You should apply if:

• You are result oriented and motivated by the impact on our customers
• You are self-motivated and thrive in a fast-paced environment
• You have great attention to detail while keeping an eye on the big picture
• You are a great communicator able to articulate complex problems
• You are able build mutual respect and trust with diverse teams and stakeholders
• You are keen to grow your knowledge in both business and technology
• Excellent SQL and Python skills with good understanding of best practices in software engineering and data engineering
• In-depth knowledge of statistical and machine learning models: logistic regression, gradient boosted trees, neural networks, survival analysis, etc
• Solid knowledge of statistics: hypothesis testing, confidence intervals, bootstrap

Even better if you have:

• Experience of managing a modelling project through its full lifecycle
• Experience in consumer lending business or similar industry

The Interview Process:

Our interview process involves 4 main stages: 

• Recruiter Call 
• Initial Call with Hiring Manager  
• Take Home Task 
• x3 final loop stage

Our average process takes around 3-4 weeks but we will always work around your availability. You will have the chance to speak to our recruitment team at various points during your process but if you do have any specific questions ahead of this please contact us on tech-hiring@monzo.com 

One of our team has written a detailed blog on their experience through this process, for extra details, hints and tips please see here.

What’s in it for you:

💰 Competitve salary ➕ plus stock options & benefits 

✈️ We can help you relocate to the UK 

✅ We can sponsor visas

📍This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London).

⏰ We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team.

📚 Learning budget of £1,000 a year for books, training courses and conferences

➕ And much more, see our full list of benefits here

Equal Opportunity Statement

We are actively creating an equitable environment for every Monzonaut to thrive.

Diversity and inclusion are a priority for us and we are making sure we have lots of support for all of our people to grow at Monzo. At Monzo, embracing diversity in all of its forms and fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog,  2021 Diversity and Inclusion Report and 2022 Gender Pay Gap Report.

We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status.

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