Mihhail Fjodorov
About Mihhail Fjodorov
Mihhail Fjodorov is a Machine Learning Engineer at Starling Bank in London, England, where he has worked since 2021. He holds a BA in Banking and Finance and an MSc in Financial Economics from the University of Leicester, and has prior experience as a Quantitative Analyst and Data Engineer.
Work at Starling Bank
Mihhail Fjodorov has been employed at Starling Bank since 2021 as a Machine Learning Engineer. His role involves developing machine learning models and algorithms to enhance the bank's services. Prior to his current position, he worked as a Data Engineer at Starling Bank from 2019 to 2021, where he focused on data manipulation and engineering tasks. His experience at Starling Bank spans a total of three years, contributing to various projects aimed at improving data-driven decision-making within the organization.
Education and Expertise
Mihhail Fjodorov holds a BA in Banking and Finance and an MSc in Financial Economics, both from the University of Leicester. His educational background provides a strong foundation in economic principles and financial systems. He possesses expertise in developing RESTful microservices using programming languages such as Java, Python, and Go. Additionally, he has experience with concurrency models, employing Akka actors in Java and Pykka actors in Python, which enhances the performance of applications.
Background
Before joining Starling Bank, Mihhail Fjodorov worked as a Quantitative Analyst at Admiral Markets Group from 2014 to 2015 in Estonia. This role involved analyzing financial data and developing quantitative models to support trading strategies. His transition from quantitative analysis to machine learning engineering reflects his adaptability and growth in the technology and finance sectors.
Technical Skills and Tools
Mihhail Fjodorov utilizes a variety of tools and technologies in his work. He employs big data tools such as Apache Spark and Apache Flink for data processing and analysis. His skill set includes data manipulation and machine learning libraries like numpy, pandas, and scikit-learn. Furthermore, he has experience with application monitoring using JMX and Jolokia, which aids in ensuring the reliability and performance of applications.