- Sketching and developing machine learning solutions (propensity modeling, ranking, recommender systems)- Implementing Online Experimentation and Multi-Arm-Bandits algorithms for strategy evaluation, exploration and exploitation.- Establishing and maintaining strong relationships with clients, ensuring high satisfaction and repeat business- Oversaw the successful delivery of projects, meeting client expectations and deadlines.- Successfully secured initial clients and projects.

- Led R&D efforts in design, deploy and scale multiple ML algorithmic pricing models (10%-30% uplift in profit)- Performed market analysis and competitive benchmarking- Conducted multible A/B tests- Ttraffic segmentation and objective maximisation therein- Sketched and conducted dynamic exploration-exploitation strategies via multi-arm-bandits (contextual MAB)- Devised new Key Performance Indicators (KPIs)- Conducted performance analyses at each in the customer funnel- Sketched and developed multiple ETL processes- Model deployment

- R&D production-level machine learning solutions pertaining to price optimization- R&D of ranking methods.- Analysis of surveillance data establishing competitive yet profitable pricing strategies, contributing to a more strategic approach in the market.- Applied machine learning models for customer clustering, identifying distinct personas and focusing on volume- or profit-generating segments.- Utilized machine learning models for demand prediction, enabling the identification of hourly abnormal performance patterns.- Spearheaded a cost-reduction project, delivering a service that scores searches based on the propensity of being booked, incorporating machine learning modeling techniques.- Maintain the in-house fraud prevention system for the internal fraud team, leveraging machine learning predictions to enhance security measures.- Developed a marketplace simulation used for identifying and testing the best-performing machine learning models through A/B testing.- Played a key role in optimizing the entire customer cycle (acquisition, maximization, retention) and contributed to Churn Modeling in Direct traffic through machine learning predictions.- Led experiment design, including A/B testing, to evaluate and refine various strategies and models.

- Orchestrated numerous simulations, refining the existing C-based algorithm to generate 3D snapshots of the solar atmosphere, focusing on a multitude of variables. Leveraged the university's supercomputer infrastructure for computational efficiency.- Executed comprehensive data filtering and analysis of substantial datasets, demonstrating adeptness in managing and analyzing big data.- Modified a tumor detection-segmentation algorithm, optimizing its functionality and performance to discern solar events effectively.- Conducted in-depth research through the systematic study, summarization, and organization of hundreds of scientific papers and bibliographic sources, contributing to the foundation of academic projects.- Assumed the role of a Teacher Assistant for four semesters, offering crucial support to professors and enhancing the academic achievements of students, specializing in computational programming.- Spearheaded and executed research endeavors, resulting in the publication of three papers (I, II, III) as a first author in one of the most esteemed journals in astrophysics.- Conceptualized and presented impactful posters at various scientific venues, proficiently conveying research findings to diverse audiences.

During my stay, I created a computer program to automatically link several lists of solar events created at ROB. The program uses temporal and spatial rules to link the different events, and it can be run in real time. This summer job was performed in collaboration with Prof. Stefaan Poedts from KU Leuven.- Spearheaded the establishment of a real-time Extract, Transform, Load (ETL) process in IDL, orchestrating the extraction of data from two public databases for immediate analysis.- Innovatively designed and implemented a feature extraction and data analysis process in IDL, demonstrating proficiency in leveraging the language for efficient data manipulation.- Engineered a streamlined real-time process that not only detected events promptly but also generated flags and populated a database in response, enhancing the overall responsiveness of the system.- Conducted rigorous statistical analyses on the observed phenomena, contributing to a deeper understanding of the underlying patterns and trends.- Reported and presented results derived from the data analysis, providing valuable insights to stakeholders and fostering informed decision-making.- Actively participated as a member in weekly meetups, engaging in collaborative discussions and contributing to the ongoing exchange of knowledge.

- Oversaw the development of an Extract, Transform, Load (ETL) process in MATLAB to facilitate the manipulation of raw images.- Applied signal filtering techniques to enhance the quality of raw data, ensuring the reliability and accuracy of subsequent analyses.- Conducted statistical analyses on extracted features, contributing valuable insights for decision-making and project advancement.- Regularly reported and presented results in collaborative meetups, fostering effective communication of findings within a team setting.- Actively participated as a member in weekly "journal club".