Who are we?
WorldRemit is changing the way people send money abroad. Weve taken something complicated & made it simple. Tap the WorldRemit App or click on our website & your international transfer is made to a bank account, cash pickup, Mobile Money, or airtime top-up. Founded in 2010, we send international remittances from 50 countries to more than 150 countries & we continue to expand our footprint.
Using WorldRemit is easy because we do the hard bit, connecting hundreds of banks, money agents, mobile operators & payment systems around the world. These were never designed to work together, but WorldRemit makes it happen.
WorldRemit has grown on average by 50% year on year & is now processing over 3bn of remittances on an annualised basis. We have raised c.$370 million in funding, currently employ over 800 employees & have offices in London, USA, Philippines, Poland, Australia, New Zealand, Canada, Japan, Hong Kong & other locations.
The journey is just beginning. We believe in faster, simpler, more accessible money transfers. That means building better products & services for our customers.
Changing the world isnt easy so we only hire the most talented people. You need to think differently, believe in new solutions to old problems, & have the drive to make them happen. We aim to attract, retain & develop people that can bring to life our values:
You can learn more about our culture & how we work by watching this video on our Careers page
About the role:
As a Machine Learning Engineer, you will be working alongside our product data scientists & data engineers to help apply machine learning throughout the business. We believe data & machine learning is key to help us provide an excellent customer experience. From offering dynamic user journeys to helping to automate manual decisions, there is a machine-learning gap at virtually every level of our organisation. You will work on the process end to end, from understanding the business problem to analysing datasets & finally putting a Machine Learning system in production. Projects you could work on: fraud prevention, anti-money laundering, marketing optimisation & attribution, customer churn prediction & retention, time-series forecasting, pricing & customer service automation.
- Liaise with stakeholders to analyse business problems & translate it as a Machine Learning application with a quantifiable metric.
- Analyse large, complex datasets to extract insights & decide on the appropriate techniques & algorithms suitable to tackle a problem.
- Collaborate with data engineers to build data pipelines & model training infrastructure.
- Build & maintain scalable machine learning inference systems in production
- Apply & adapt state of the art research to solve business problems.
- Research & implement best practices to improve the existing machine learning training & serving infrastructure.
- Effectively communicate results with the team, & stakeholders.
- Provide machine learning domain expertise to support to engineers & product managers in structuring the implementation of machine learning systems.
- Masters/PhD in Machine Learning, Computer Science or a related quantitative field (or equivalent experience)
- Solid understanding of Machine Learning fundamentals & learning theory
- Experience in the Machine Learning model building & evaluation process
- Ability to translate business requirements into machine learning solutions
- Strong software engineering experience in Python
- Experience with SQL
- Experience in one or more in the following: fraud prevention, anti-money laundering, marketing optimisation & attribution, customer churn prediction & retention, time-series forecasting, pricing & customer service automation
- Experience building & maintaining data extraction, feature processing pipelines & machine learning models in a production environment
- Deep understanding of Machine Learning Algorithms & Libraries to allow for customisation towards business requirements
- Experience in statistical experiment design & performance analysis of machine learning models
- Understanding of Data Engineering & Big Data technologies (Hadoop, Spark, Kafka etc...)
- Experience working in the Amazon Web Services stack
- Life assurance of 3 times your salary, should the worst happen.
- Pension scheme offering 8% matched contributions.
- Private medical & dental care plans.
- 25 days of holiday plus bank holidays, rising to 28 after 3 years.
- Free breakfast & fruit every day & Friday 'afternoon tea' drinks & nibbles.
- No formal dress code.