Think Forward! Our purpose is to empower people to stay a step ahead in life and in business. We are an industry recognized strong brand with positive recognition from customers in many countries, a strong financial position, Omni-channel distribution strategy and international network. If you want to work at a place where we believe that you can make the difference by using machine learning to generate data driven products and solve the most pressing business challenges, please read on.
We are incredibly excited about Data Analytics and the great potential for progress and innovation. We believe that analytics is a key differentiator in bringing “anytime, anywhere, personalized” services to our customers. We wish to improve our operational processes and create new and innovative data driven products that go beyond traditional banking, such as the platform models. Achieving this vision will require us to build and expand on our analytics effort and organize ourselves around focused value buckets with strong coordination capabilities of data, technology, customer journey, UX, as well as external partnerships.
People Analytics is a young, international, and fast-moving team with the mission to make all people related decisions in ING data-driven, a fun environment where there is lots of opportunities to develop yourself. We are jointly part of ING Global HR and ING Analytics which is a new unit responsible for realizing this vision for ING, differentiating ING as a leader in data-driven organization, within the banking sector and beyond.
We are looking for you:
You will be part of Data Science Chapter and you will be coached by a senior data scientist. People Analytics offers a lot of opportunities; please see below for a list of three topics that might inspire you to choose a topic that speaks to your heart and has an impact for ING.
Are you keen to know more or apply?
For further inquiries, please contact Ehsan Mehdad at
We are looking forward to getting to know you!
Potential thesis topics in People Analytics:
Deep reinforcement learning and simulation:
Reinforcement learning is a rapidly developing branch of machine learning. Some of the recent mind-blowing achievements in AI are a result of the exponential growth made in deep reinforcement learning. While deep reinforcement learning is a new development in the world of artificial intelligence, and still mainly considered a research topic, simulation modelling has been in daily practical use for decades. It has a very mature community with a vast body of real-world examples. Common practice in the simulation community is to take simulation models, run experiments (Optimization, Monte Carlo, parameter variation, etc.) and use the outputs to make better decisions about a model’s real-world counterpart. With this approach, a human is needed to experiment with the simulation model and get information from it, read . We in the people analytics team are interested to see how we can use deep reinforcement learning techniques to find the best scenario in the workforce decision support system which uses a simulation model for the dynamics of internal workforce supply (hire, stay, move, promote, leave, and retire)
Causal Inference and online experiments:
In people analytics we are interested to apply behavioural science insights and methodologies to bring employees’ voice to the senior management and make the follow-up decisions fact driven. One of the most exciting areas we want to work on is the usage of causal inference together with the online organizational experiments. Causal inference is a category of statistical methods that is commonly used in behavioural science research to understand the causes behind the results we see from experiments or observations, read . In our continuous listening squad, we are exploring the use of experiments, games, and surveys to take employees’ pulse on different matters. We are interested to see how causal inference may explain the ‘whys’ in the experiments that we will run.
Knowledge graphs (KGs) & Named Entity Recognition (NER) and Relation Linking:
Knowledge Graphs show real benefits in many domain of NLP, specific example are: Named Entity Recognition, Entity Linking, Relation Extraction, and Relation Linking, see . We are particularly interested in neural approaches that allow to build knowledge graphs from text data sources. We have many scattered data silos (mainly unstructured text, examples are: resumes, profiles, IT tickets, forums, learning portal, etc.) in and out of the bank which can provides a 360-degree view of bank’s employees, the linkage of these scattered sources into a graph structure will breaks the silos and significantly improves the employee experience in the bank.
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