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My Curriculum Vitae

Professional Career

2018-09 to 2019-07

Data Science Intern

Yunify, Geleen, The Netherlands

Grew the data-driven culture within the company

Create predictive model with XGBoost which performed 15% better than doctors at predicting treatment success.

Generated client profiles allowing clinics and health-care professionals to gain more in-depth insights on their patients and improve treatment design.

Worked closely with stakeholders and field experts to understand their needs and validate models.


2017-08 to 2018-07

Backend Developer And Data Science Intern

Stimuliz, Heerlen, The Netherlands

Improved ease of imports for end users and reduced number of errors from manual imports by automatizing data imports, using image processing in Python.

Contributed to application’s backend refactoring using the PHP framework Phalcon to improve source code’s modularity and maintainability.

Optimised application’s search feature with refined SQL queries.

Improved end user experience and allowed fast bulk exports by building module to export client’s data as formatted PDFs.

Improved API reliability by writing unit tests and increased application coverage to more than 85%.

2017-07 to 2017-08

Artificial Intelligence Intern

Accenture, Heerlen, The Netherlands

One of select few that participated in Accenture’s Artificial Intelligence Summer Camp.

Worked in inter-disciplinary team to develop Proof Of Concept application on Pepper robot.

Worked on making Pepper able to sustain conversation with hypothetical clients of clothing store to guide them to desired article.

Academic Career

2016-09 to 2019-07

Bachelor of Science: Data Science And Knowledge Engineering

Maastricht University – Maastricht

Coursework in Data Analysis, Machine Learning, Probabilities And Statistics and Software Engineering.

Completed interdisciplinary research project reserved for high achieving students from Maastricht University as part

of Honor+ Program.

Worked at two companies as part of KE@Work Honor’s Program

Thesis: Conformal Weighted Semi-Supervised Learning For

Correcting Selection Bias In Classification