Senior Machine Learning Engineer
Who are we?
WorldRemit is changing the way people send money abroad. We've taken something complicated and made it simple. Tap the WorldRemit App or click on our website and 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 and we continue to expand our footprint.
Using WorldRemit is easy because we do the hard bit, connecting hundreds of banks, money agents, mobile operators and 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 and is now processing over £3bn of remittances on an annualised basis. We have raised c.$370 million in funding, currently employ over 800 employees and have offices in London, USA, Philippines, Poland, Australia, New Zealand, Canada, Japan, Hong Kong and other locations.
The journey is just beginning. We believe in faster, simpler, more accessible money transfers. That means building better products and services for our customers.
Changing the world isn't easy - so we only hire the most talented people. You need to think differently, believe in new solutions to old problems, and have the drive to make them happen. We aim to attract, retain and develop people that can bring to life our values:
You can learn more about our culture and how we work by watching this video on our Careers page
https://www.worldremit.com/en/careers . About the role:
As a Machine Learning Engineer, you will be working alongside our product data scientists and data engineers to help apply machine learning throughout the business. We believe data and 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 and finally putting a Machine Learning system in production. Projects you could work on: fraud prevention, anti-money laundering, marketing optimisation and attribution, customer churn prediction and retention, time-series forecasting, pricing and customer service automation. Responsibilities:
- Liaise with stakeholders to analyse business problems and translate it as a Machine Learning application with a quantifiable metric.
- Analyse large, complex datasets to extract insights and decide on the appropriate techniques and algorithms suitable to tackle a problem.
- Collaborate with data engineers to build data pipelines and model training infrastructure.
- Build and maintain scalable machine learning inference systems in production
- Apply and adapt state of the art research to solve business problems.
- Research and implement best practices to improve the existing machine learning training and serving infrastructure.
- Effectively communicate results with the team, and stakeholders.
- Provide machine learning domain expertise to support to engineers and 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 and learning theory
- Experience in the Machine Learning model building and 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 and attribution, customer churn prediction and retention, time-series forecasting, pricing and customer service automation
- Experience building and maintaining data extraction, feature processing pipelines and machine learning models in a production environment
- Deep understanding of Machine Learning Algorithms and Libraries to allow for customisation towards business requirements
- Experience in statistical experiment design and performance analysis of machine learning models
- Understanding of Data Engineering and 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 and dental care plans.
- 25 days of holiday plus bank holidays, rising to 28 after 3 years.
- Free breakfast and fruit every day and Friday 'afternoon tea' drinks and nibbles.
- No formal dress code.