Data Scientist (Fincrime)

Starling is the UK’s first and leading digital bank on a mission to fix banking! Our vision is fast technology, fair service, and honest values. All at the tap of a phone, all the time.

Starling is the UK’s first and leading digital bank on a mission to fix banking! We built a new kind of bank because we knew technology had the power to help people save, spend and manage their money in a new and transformative way.

We’re a fully licensed UK bank with the culture and spirit of a fast-moving, disruptive tech company. We’re a bank, but better: fairer, easier to use and designed to demystify money for everyone. We employ more than 3,000 people across our London, Southampton, Cardiff and Manchester offices.

Our technologists are at the very heart of Starling and enjoy working in a fast-paced environment that is all about building things, creating new stuff, and disruptive technology that keeps us on the cutting edge of fintech. We operate a flat structure to empower you to make decisions regardless of what your primary responsibilities may be, innovation and collaboration will be at the core of everything you do. Help is never far away in our open culture, you will find support in your team and from across the business, we are in this together!

The way to thrive and shine within Starling is to be a self-driven individual and be able to take full ownership of everything around you: From building things, designing, discovering, to sharing knowledge with your colleagues and making sure all processes are efficient and productive to deliver the best possible results for our customers. Our purpose is underpinned by five Starling values: Listen, Keep It Simple, Do The Right Thing, Own It, and Aim For Greatness.

Hybrid Working

We have a Hybrid approach to working here at Starling - our preference is that you're located within a commutable distance of one of our offices so that we're able to interact and collaborate in person. We don't like to mandate how much you visit the office and work from home, that's to be agreed upon between you and your manager. 

Our Data Environment

Our Data teams are excited about the value of data within the business, powers our product decisions to improve things for our customers and enhance effective and agile decision making, regardless of what their primary tech stack may be. Hear from the team in our latest blogs or our case studies with Women in Tech.

We are looking for talented Data Scientists with experience working in Financial crime and Anti-money laundering to help the bank solve complex problems using Machine Learning. This role will be broad in scope and you can expect to collaborate with many teams in order to advise, support and develop processes using the most appropriate methodology for the topic in hand. If you love building Machine Learning based solutions in an R&D driven environment then we’d love to hear from you.

Responsibilities:

• You will be part of a team delivering data driven solutions and insights to improve the speed, efficiency, and quality of decision-making
• Work proactively with technical and non-technical teams to deliver insights to support the wider business
• Build, test and deploy machine learning models which will improve and/or automate decision making
• Provide insightful analytics across the bank to assist with decision making
• Engage with Engineering teams to ensure we capture data points that are relevant and useful for insights and modelling

We’re open-minded when it comes to hiring and we care more about aptitude and attitude than specific experience or qualifications. We think the ideal candidate will encompass most of the following:

• Demonstrable industry experience Data Science/Machine Learning in one or more of:
• Financial Crime
• Anti-money laundering
• Transaction monitoring
• Anomaly detection
• Excellent skills in Python and SQL
• Experience with libraries such as Scikit-learn, Tensorflow, Pytorch
• Strong data wrangling skills for merging, cleaning and sampling data
• Strong data visualisation and communication skills are essential
• Understanding of the software development life cycle and experience using version control tools such as git
• Demonstrable experience deploying machine learning solutions in a production environment

Desirables:

• Experience with AWS/GCP
• Desire to build explainable ML models (using techniques such as SHAP)

Interview process

Interviewing is a two way process and we want you to have the time and opportunity to get to know us, as much as we are getting to know you! Our interviews are conversational and we want to get the best from you, so come with questions and be curious. In general you can expect the below, following a chat with one of our Talent Team:

• Stage 1 - 30 mins with one of the team
• Stage 2 - Take home challenge
• Stage 3 - 90 mins technical interview with two team members
• Stage 3 - 45 min final with an executive and a member of the people team