ESKAPADE is a self-learning software framework for typical machine learning problems. It employs a
modular set up in the entire Data Science workflow: from data ingestion to transformation to trained
model output. Every part of the analysis can be run independently with different parameters from one
python configuration file. The modular working approach reduces the complexity of your analysis and
greatly simplified the reproducibility and reusability of all analysis steps.
Max joined the KPMG Big Data team in 2015 and has a long background in the purely data-driven field of experimental particle physics, He obtained a PhD in High Energy Physics working at Stanford, and afterwards worked for 8 years at one of the major CERN experiments. Through this work, Max has acquired advanced skills for handling and analyzing large data sets. Within our team Max has a special interest in helping other organizations and companies to become more data-driven.
Max has over 12 years of experience in the development and teaching of analysis software, software management, and data quality assurance. He is an expert in statistical data modeling and analysis, and as such has coordinated and reviewed many big data analyses, including the discovery statement of the Higgs boson particle at CERN.
At KPMG Max is involved in the development, testing and deployment of common solutions for big data analyses.
Sander has over 15 years of experience in large scale distributed computing,real-time systems and data processing technologies.He is responsible for new product development and global roll out of Big Data services at KPMG.
He holds a PhD in High Energy Physics (HEP) and worked at CERN, generally accepted as the cradle of Big Data processing in the world. He received a number of grants and awards related to high performance distributed computing and professor in Big Data Ecosystems at the University of Amsterdam.
Sander has been the lead of the Big Data services team during its incubation. He built a team of former CERN scientists to deliver industry solutions in Big Data. Current engagements of the team include operation al strategy, workshops, privacy analyses, sourcing strategies, turn-key solutions and more.
He is publishing regularly about the impact of Big Data on business and society in well known papers and magazines and is frequently invited as keynote speaker on major events in science and industry.
Martijn is an expert on analysing, processing, and modelling of large quantities of data. Moreover he has a great affinity with big data enabling technologies.
He holds a PhD in High-Energy Physics and worked for several years as a researcher on physics analyses and Monte Carlo simulations for the ATLAS and CMS experiments at CERNâ€™s Large Hadron Collider. As an advisor and data scientist at KPMG he now helps other organisations in becoming more data-driven.
Through his research career, Martijn has a strong background in statistics, analysis algorithms, and distributed computing . He gained a lot of international experience by working at several large European research laboratories (e.g. CERN., DESY).
At KPMG he worked on various data analytics projects with clients. Among them a Dutch telco, exploring the added value of Big Data, an academic hospital, validating new operation room schedules with Monte Carlo simulation techniques, a media company, applying machine learning to identify online user interests and optimize advertisement w.r.t. this, and a bank in Taiwan, optimizing a credit card remediation campaign strategy.
Lodewijk graduated in Theoretical Physics at the University of Amsterdam in 2013, where he specialized in solving unstable mathematical problems using numerical methods. He has a more abstract background than the rest of the team with a more mathematical focus. This allows him to make the analyses concrete when it comes to the underlying logic and math.
Lodewijk provides insight into complex issues using various visualizations. This makes it easier to understand large and complicated problems a client might face, such that a clear solution may be implemented.
Before joining KPMG Lodewijk was a teaching assistant in mathematics and physics at the University of Amsterdam for over 3 years, teaching students as well as high school teachers.
At KPMG Lodewijk has done analytical work in several sectors including Healthcare, Retail and Insurance. He has experience handling data ranging from geodata to payment data to patient data.