ING’s goal is to enable people to “make the difference” and empower them to stay a step ahead in life and in business. We are one of the largest banks in Europe and we continuously evolve to become one of the most innovative companies in the banking sector. 

The ING Analytics group is a major driving force in ING’s transformation, aimed at helping us to become a data-driven digital leader and creating tangible value for ING and its customers through world-class analytics models, products, and services.

Retail Banking Analytics Data Science Chapter

The Retail Banking Analytics Data Science Chapter provides ING with machine learning expertise for the development of the lending, pricing and personalization analytics. We are based in Amsterdam and consist of 10+ highly-skilled and talented Data Scientists from diverse nationalities and backgrounds (such as physics, statistics, maths, computer science, econometrics, astrophysics). We work in a fun and creative environment, and we are dedicated to bringing out the best in both each other and our projects through collaboration and knowledge sharing. The portfolio of projects is broad and uses a wide range of algorithms and tools, but all relate to risk and pricing. In short, we offer a world-class working environment for data scientists to make an impact and never stop learning.

What you’ll do

As a Data Scientist, you will be

• developing machine learning and advanced analytics solutions for a specific business use case,
• working together with other data scientists,
• actively engaging with stakeholders,
• collaborating with subject matter experts and machine learning engineers to bring your advanced models to production.

Who are you?

• You are highly motivated when solving complex problems and have an always-learning attitude. You keep on top of the latest developments in machine learning and apply them in your projects where they bring value.
• You enjoy sharing knowledge with your teammates and helping others improve alongside you.
• You have a solid and broad background in machine learning, especially in classification and regression. You have experience with algorithms such as random forests, gradient-boosted decision trees, logistic regression, and neural networks.
• You enjoy coding. You have strong Python and software engineering skills, and you know your way around the linux shell. You also know how to use git for version control and SQL dialects for interacting with databases.
• You’re willing to learn: you are self-reflective and open to constructive criticism on both the technical and interpersonal level.

Required skills

• At least 5 years of work experience in the data science field, ideally in the private sector. Knowledge of lending, credit risk, and credit decision domains is a strong plus.
• Broad experience with end-to-end machine learning project implementation. Involvement in all modelling steps from data extraction to modelling and deployment.
• Previously led multiple data science projects
• Strong Python and Spark programming skills. Hands on experience and proficiency in MS Azure Devops and GCP is a strong plus.
• Having a very solid educational background that includes mathematics and statistics (M.Sc. or Ph.D.).
• Strong theoretical knowledge of statistical modelling and machine learning, and a demonstrated ability to apply these to solve business problems and develop innovative models and/or data-driven products.
• Excellent written and oral communication skills in both technical and business contexts.
• Fluency in spoken and written English.

What we offer

• An opportunity to become part of an international and ambitious team of highly-skilled, talented people who love working collaboratively.
• A full-time position (40 hours / week).
• An excellent package including 13th month, holiday allowance, and personal choice budget.
• A very stimulating intellectual environment as part of the ING Analytics community.
• Exciting, impactful and highly-visible projects in a global organization where advanced analytics are a strategic priority.
• A MacBook Pro, plus an allowance for a work mobile and your home office setup.
• Opportunity to travel to cities such as Brussels, Frankfurt, Berlin, Milan, Madrid etc.

Our Interview Process

Our recruitment strategy has two steps;

Interview 1: A technical interview focusing on motivation, machine learning skills, data science experience and knowledge. This usually takes 60 mins.

Interview 2: Candidates who are successful in the first interview will be invited for a case study. The case study consists of solving a business problem, making use of several datasets that will be shared via gitlab. The assignment is to be finished in 7 calendar days.  The output of the assignment is a brief presentation, as well as accompanying code.

About ING’s Growing the Difference strategy

A core part of ING’s identity is about being entrepreneurial, collaborative and customer obsessed. This, and the great work we did ‘making the difference’ over the past years, has made us a highly successful bank and ready to grow.

If you want to work in an environment where you believe that you can make a difference by using machine learning, advanced analytics to generate data-driven solutions and solve the most pressing business problems, then this position is for you.

We are incredibly excited about Advanced Analytics and the great potential for making the difference. We believe that analytics is a key differentiator in bringing “anytime, anywhere, personalized” services to our customers. We want to improve our operational processes and create new and innovative data-driven solutions that go beyond traditional banking. Achieving this vision requires us to build and expand on our analytics effort and organize ourselves around focused-value buckets with strong coordination capabilities in data, technology, customer journey, and UX, as well as external partnerships.