Data Scientist/Machine Learning Engineer Fraud Detection
Join our Tribe Fraud as a Data Scientist/Machine Learning Engineer and help protect our customers and society from fraud!
Within our Tribe Fraud NL you will work in a squad of Data Scientists focusing on fraud detection. We detect fraud in all channels of ING retail bank, including cards, online banking and mobile banking. The environment and challenges we face in the field of fraud change daily. Our goal is to be always one step ahead. With every new iteration and every new insight, we make the bank safer and protect our customers from fraud.
The Data Science squad is responsible for developing and maintaining fraud detection models. We strive to make these future-proof and scalable, and have them complement expert-based rules. The team is an ambassador of data science in the fraud domain and actively participates in the data science community. In this team you will contribute to ING being the safest bank and a leading player in fraud detection.
Our data science workflow has been maturing over the years, with higher standards for coding, testing and documentation. Model development, deployment and monitoring has been significantly improved. However, there is still plenty of room for improvement, which is why we are looking for a Machine Learning Engineer to take our data science to the next level.
Roles and responsibilities
We are looking for a medior or senior Machine Learning Engineer to enable data science in fraud detection. You will work with data scientists to develop machine learning models and other data science solutions, while having a focus on the engineering aspects. This includes:
- building and/or maintaining:
- machine learning models;
- tooling for model development;
- data processing and validation;
- pipelines for model deployment
- model performance monitoring;
- working with other teams to integrate solutions into production systems, such as a real-time fraud detection engine;
- applying best practices, such as testing, version control, four-eyes, and coding standards;
- providing support to and explaining best practices to colleagues;
- documenting models and other components;
- doing administration to comply with data, model and IT governance;
- being aware of trends in fraud and banking.
The team works in a hybrid way, meeting up together in Amsterdam for one day a week, while working remotely the rest of the week.
How to succeed
We hire smart people like you for your potential. Our biggest expectation is that you’ll stay curious. Keep learning. Take on responsibility. In return, we’ll back you to develop into an even more awesome version of yourself.
Ideally you have an MSc or PhD degree with excellent academic results in the field of Physics, Mathematics, Computer Science, Artificial Intelligence or similar. You have strong analytical skills, affinity with IT and the ability to learn fast. You are fluent in English, fluency in Dutch is a plus.
As a Machine Learning Engineer in fraud detection, it is important to understand and have experience with most of the below.
- Machine Learning: a grasp of the workings and applicability of popular machine learning methods and algorithms. Experience with popular frameworks such as numpy, scikit-learn, SparkML.
- Languages: Python (OOP), SQL, Java, bash.
- Technologies: Jupyter Notebooks, PySpark, SQL, Airflow, Azure DevOps, Apache Kafka, Linux, RiskShield.
- Practices such as unit/integration testing, DTAP, Continuous Delivery or Continuous Deployment processes.
- Experience working with big data and query/code optimization practices.
- Domain knowledge: knowledge of cybercrime, cybersecurity, banking or even specifically fraud in retail banking is a plus.
We ask for 3+ years of experience in a Machine Learning Engineering role or similar.
More generally we look for a colleague with a talent for taking it on and making it happen, enthusiasm for helping others to be successful and a knack for always being a step ahead. In other words, you strive to bring fresh ideas to life and embrace challenges in a fast changing and complex environment. You are a naturally collaborative person who listens and invests in others to achieve common goals. You love to challenge the status quo and are eager to propose and build creative solutions to problems.
Rewards and benefits
We want to make sure that it’s possible for you to strike the right balance between your career and your private life. You can find out more about our employment conditions at https://www.ing.jobs/netherlands/Why-ING/benefits.htm
The benefits of working with us at ING include:
- A salary tailored to your qualities and experience
- 24-27 vacation days depending on contract
- Pension scheme
- 13th month salary
- Individual Savings Contribution (BIS), 3.5% of your gross annual salary
- 8% Holiday payment
- Hybrid working to blend home working for focus and office working for collaboration and co-creation
- Personal growth and challenging work with endless possibilities
- An informal working environment with innovative colleagues
With 60,000 employees and operations in approximately 40 countries, there is no shortage of opportunities for people with initiative who want to help people take a step ahead in life and in business. Do you want to work at the cutting edge of what’s possible and at the same time ensure you work with integrity and hold the customer’s interests at heart? Do you want to be surrounded by progressive, inspiring, diverse and supportive colleagues? Then there is no better place to invest your talents than at ING. Join us. Apply today.
Contact the recruiter attached to the advertisement for more information. Want to apply directly? Please upload your CV and motivation letter by clicking the “Apply” button.
In your motivation letter we would like you to answer the questions below:
- What do you believe are the biggest challenge(s) of bringing machine learning to production in the domain of fraud detection?
- What are your ideas on dealing with these challenge(s)?
- Can you share a relevant achievement that you are proud of? For example, your contribution to a data science project that went live.
Please note, that multiple interview steps involving various business stakeholders will be part of the selection process, as well as an enhanced screening for this sensitive position.