Business Banking Data Scientist
ING
Brussel, Belgium
about 2 months ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
Tech stack
Artificial Intelligence
Python (Programming Language)
Machine Learning
SQL Databases
Apache Spark
Git
Information Technology
Machine Learning Operations
Job description
A day in the life of a Data Scientist III:
- The Data Scientist will closely monitor and contribute to the development and execution of a key project in collaboration with external consultants, ensuring sufficient knowledge transfer and readiness to assume full ownership upon project delivery completion.
- Trying different approaches until you succeed if the problem is hard to crack or a technical solution hard to introduce.
- Making original solutions work in real-life by looking at things from a different perspective. Next, to being creative, you are also persistent.
- Working in cross-functional teams with ambitious goals, asking fellow team members for assistance, but also helping them out by sharing your knowledge and capabilities.
- Collaborating with many different parties throughout our organization enabled by your solid communication skills.
- Mobilizing people to capture the tremendous value that Data Science brings to customers of ING via your enthusiasm and passion that are contagious
Requirements
Do you have experience in Spark?, Do you have a Master’s degree?, 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.
- Machine learning, AI & data science knowledge to be applied on impactful projects.
- You have at least 5 years of relevant applied experience
- MSc or Ph.D. with excellent academic results in the field of Computer Science, Machine Learning, Mathematics, Statistics, or other quantitative fields
- Analytical and content strength (in DS work field): sees which DS techniques apply to the business problem at hand, able to structure the approach and a good programmer.
- Technical efficiency to create production ready models and roll them out.
- Knowledge of Python, Spark, SQL; GCP & other cloud providers, GIT, ML algorithms, Model lifecycle.
- Stakeholder management and strong communication skills.
- Financial and banking knowledge as well as how it translates to analytic
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