Ken Tang

Ken Tang's work experience begins in 2014 as a Teaching Assistant at the University of Vienna. In 2014 and 2015, they served as an Intern in Group Actuary roles at UNIQA and then became a Credit Portfolio Analyst at Raiffeisen Bank International AG. In 2017, Ken joined PwC as an Associate and later became a Senior Associate. Ken then joined Morgan Stanley in 2019 as an Interest Rate Desk Strategist through a training program from UC Berkeley. In 2020, Ken participated in The Data Incubator's two-month data science fellowship as a Full Scholarship Fellow. Ken also worked as a Teaching Assistant at the University of California, Berkeley during the same year. In 2021, Ken became a Quantitative Analyst at ICE, and they currently hold the position of Quantitative Research Associate at Link Logistics Real Estate.

Ken Tang has a Master of Financial Engineering degree from the University of California, Berkeley, which they obtained from 2019 to 2020. During this time, their field of study focused on Financial Engineering and Machine Learning.

Prior to their time at UC Berkeley, Ken Tang earned a Master of Science (M.Sc.) degree in Quantitative Economic and Finance from the University of Vienna, from 2014 to 2017. Ken also holds a Bachelor of Science (B.Sc.) degree in Statistik from the same university, earned from 2011 to 2014.

In addition to their formal education, Ken Tang has obtained several certifications. In 2023, they completed the C++ Developer certification from Udacity. Ken also obtained the AI for Trading certification from Udacity in 2023. In 2020, Ken Tang completed a Fellowship with The Data Incubator. In 2019, they completed the C++ Programming and Numerical Analysis Course offered by UC Berkeley Executive Education. Furthermore, Ken Tang obtained the Advanced R Programming certification from Coursera in the same year. In 2019, they also completed the Deep Learning certification from deeplearning.ai.

Based on the provided information, it can be concluded that Ken Tang has a strong educational background in financial engineering, machine learning, quantitative economics, finance, statistics, economics, and sinology.