Edmond Mitchell

Edmond Mitchell has extensive experience in the field of data science and analytics. Edmond is currently working at Future Anthem as the Head of Data, where they lead the data team and drives data-related initiatives for the gambling industry. Prior to this, Edmond was a Lead Data Scientist at Future Anthem, where they developed cloud solutions to optimize game portfolios and increase player engagement.

Before their role at Future Anthem, Edmond worked as a Contract Data Scientist for himself. Edmond also gained valuable experience at PhotoBox as a Team Lead Analytics and Senior Data Scientist. At PhotoBox, they focused on developing models to drive commercial value and implemented a data-driven approach to business decisions.

Edmond's previous experience includes working as a Data Scientist at Rank Group, where they specialized in "Responsible Gambling" and created machine learning models to identify problematic behavior. Edmond effectively communicated ideas and solutions to technical and non-technical audiences in this role.

Earlier in their career, Edmond worked as a Postdoctoral Researcher at Insight - Dublin City University, where they developed advanced classification models using real-time sensor data. Edmond also worked as a PhD Student at the same institution, where they advanced machine learning technology for the automatic classification of physiological data from wearable sensors.

Edmond's career began as a QA Engineer at Ocuco Limited, where they gained experience in quality assurance. Throughout their career, Edmond has demonstrated expertise in data science, machine learning, and developing innovative solutions to drive business value.

Edmond Mitchell completed their education starting from 2005 to 2009 at Maynooth University, where they obtained a Bachelor of Engineering (BEng) degree in Electrical and Electronics Engineering. Following this, they pursued further education from 2010 to 2014 at Dublin City University, obtaining a Doctor of Philosophy (PhD) degree with a specialization in Machine Learning.