Pei-Chun Hsieh

Recently performing financial data analyses at a financial data science lab. A total of 7 years of experience as Financial Data scientist, Finance Lead and Tax Auditing Associate, achieving success in promoting effective communication as a liaison among stakeholders. Experienced with financial data analysis, financial reporting, management accounting and auditing.

In my previous position as a Local Finance Lead, I cooperated with cross-functional department heads at all levels to propose solutions for regional and local issues. SDL (now merged by RWS) was a localisation translation and technology company. In this role, I oversaw all financial affairs for four South-Eastern entities. I re-designed the financial frameworks, streamlining the procedures to ensure strong local controls and reducing the execution time by more than 50%. Also, I have worked on analysis projects involving large amounts of financial data, which are ultimately translated into financial insights to support regional and operating managers in forming strategic decisions.

Currently, I am the Lead Financial Data Analysts at Sociovestix Labs to perform big data analysis on the Executive-Gender Power Gap and present research findings to stakeholders and potential partners. This experience has equipped me with a multitude of data analytic skills including data analysis, data visualisation, and machine learning using SQL, Python, Power BI and Superset.

Master of Science in Finance and Financial Technology (Distinction)

ICMA Centre, University of Reading, UK

• Core Modules: Quantitative Finance, Financial Derivative, Machine Learning, Big Data, Blockchain, and Cryptocurrencies
• Projects:
  • Business Intelligence Analysis on Manchester Airbnb letting market under pandemic using SAS, identifying market issues, and developing Machine Learning models (KNN, Decision Tree, etc) to predict the trends
  • Customer Segmentation Analysis on potential bond-buyers using Machine Learning (Random Forest, etc)
  • Stop-loss Trading Strategy Analysis and Back-testing on security market using Python and SQL