Michalis Christofi, PhD

Michalis Christofi, PhD has a diverse work experience in data science and consulting. Michalis currently holds the position of Senior Data Scientist at Insight Capital Partners since January 2020. Prior to this, they worked as a Senior Consultant and Data Scientist at Electi Consulting from March 2019 to present, where they were involved in R&D, designing AI strategies, exploring client data for opportunities, and developing machine learning infrastructure. Michalis also audited existing data pipelines and optimized solutions for bias, performance, and scalability. Some of their notable clients include the European Central Bank and Mediterranean Shipping Company.

Before joining Electi Consulting, Michalis worked as a Senior Data Scientist at dataffirm from January 2016 to January 2020. Here, they developed trading signals and custom machine learning models for the company's investment strategy. Michalis also gained experience as a Data Scientist at Kantar Media from September 2014 to January 2016, where they contributed to the award-winning SKO project, designing algorithms for user behavior.

Michalis pursued their Ph.D. in Algorithmic Design from King's College London from October 2010 to September 2014. Michalis also has experience in data analysis from their role at Europcar Cyprus in 2012 and served as an Assistant Manager at P&S Carton Industries in 2010.

Michalis Christofi, PhD has an extensive education history in the field of Computer Science and Mathematics. Michalis completed their Ph.D. in Computer Science from King's College London from 2010 to 2013. Prior to that, they obtained their MSc in Operational Research from The London School of Economics and Political Science (LSE) from 2009 to 2010. Michalis also holds a BA degree in Mathematics from the University of Cambridge, which they completed from 2006 to 2009.

In addition to their academic degrees, Michalis has acquired several certifications. Michalis obtained a series of Coursera Verified Certificates including "Developing Data Products," "Regression Models," "Practical Machine Learning," "Exploratory Data Analysis," "Getting and Cleaning Data," "Reproducible Research," "Statistical Inference," "R Programming," and "The Data Scientist's Toolbox" in 2014.