Data & AI - Data Science/ML Engineering Graduation Internship
Role details
Job location
Tech stack
Job description
Are you looking for an internship where you play an active role in formulating a vision on the latest digital developments? Do you want to do your graduation research on a topic that is directly relevant in the business world? Accenture offers a full-time graduation internship for 5 - 9 months, depending on the duration of your graduation project. If you already have a research question or area in mind, please share it with us!
Your role:
Being an intern in our Data & AI team means we will shape your internship together. What topics might you consider if you apply to our group? We are interested in developing and implementing new technologies and innovations for Data Science, Machine Learning Engineering, and Generative AI. The thesis possibilities are endless, and we are eager to brainstorm with you to find a topic that best matches your interests.
Some examples of previous graduation projects within Data & AI include:
· Building an Explainable AI visualization tool to visualize counterfactuals (features that when changed change the prediction outcome) to explain the predictions of Machine Learning models in a business context.
· Using a deep learning computer vision technique to automatically generate vectorized floorplan images that can be used by computer tools.
· Combining a regression model with a random forest and using panel data to predict customers who are likely to churn at an investment firm, while accounting for an imbalanced dataset.
So, what does a "day in the life of a Data & AI intern" look like? While we expect you to be independent and take the initiative in shaping your daily activities, these will include, among other things:
· In the case of a graduation internship: writing your thesis at our office. You will have access to all Accenture resources for background information, and the opportunity to talk with our colleagues who are happy to discuss your thesis topics with you.
· Have the opportunity to get hands-on experience as a consultant by supporting on client projects
· Expanding your network and knowledge by attending team meetings, events, mentor sessions, trainings, guest lectures, and one-on-one coffee chats.
At the end of your internship, you will present the results of your thesis or the experience you gained during your work internship at one of our team meetings. We consider this internship as the ideal opportunity to see if there is a match on both sides for a permanent position after completing your time as an intern. As one of our analysts (junior consultant) says
Requirements
Who you are:
· You are currently a master's student enrolled at a Dutch university for the entire duration of your internship.
· You are nearing the completion of your master's degree at university and are looking to write a thesis in your field of interest at a company, with growth opportunities in the future.
· You are analytically strong and can solve complex problems or obtain strategic insights in a data-driven manner.
· You have a background in Computer Science, Data Science, Artificial Intelligence, Statistics, or a similar field.
· You have demonstrable affinity and/or experience with trends and developments in data science, computer science, or machine learning.
· You have experience with relevant programming languages (Python, SQL, etc.), machine learning techniques (Neural Networks, Random Forest, etc.), frameworks (PyTorch, Sklearn, etc.), and visualization tools (PowerBI, Matplotlib, etc.)
· You are fluent in English, both spoken and written.
Bonus Points:
· You have participated in extracurricular activities such as a board year, committee, or internship.
· You are a team player with excellent communication skills; you are professional and precise.
· You can work independently and have a hands-on mentality; you are creative and take responsibility.
· You are familiar with the latest technologies, with experience or knowledge of cloud platforms (such as AWS, Azure, Google Cloud) and big data (e.g., Hadoop, Spark).