Yipeng Wang

AI Researcher

Shanghai, China2 yrs 11 mos experience

Highly Stable

Key Highlights

Expert in quantitative research and investing.
Proven track record in fraud detection using AI.
Strong background in data analysis and pipeline optimization.

Stackforce AI infers this person is a Fintech Data Scientist with expertise in quantitative analysis and machine learning.

Contact

Skills

Core Skills

Quantitative ResearchQuantitative InvestingData Analysis

Other Skills

Machine LearningStatistical MethodsSQLPython (Programming Language)PythonPysparkEnglishCommunicationResearchOracle DatabaseR (Programming Language)C++Problem SolvingPresentationsLinux

About

Email: wangyipeng07@gmail.com WeChat: wyp8726

Experience

2 yrs 11 mos

Total Experience

2 yrs 11 mos

Average Tenure

Current Experience

Trexquant investment lp

Quantitative Researcher

Mar 2023 – Present · 3 yrs 1 mo · Shanghai, China

Explore, process, and load data sets into the system to develop signals for systematic quantitative strategies;
Develop market-neutral and medium-frequency alphas that predict future return;
Combine alphas to make final quantitative strategies that can be traded in the market by machine learning and statistical methods;
Involved markets include equity markets in US and China and futures markets in US.

Quantitative InvestingQuantitative ResearchData AnalysisMachine Learning

Grab

Data Science Intern

Aug 2022 – Dec 2022 · 4 mos · Singapore

Through the data of user's action on the Grab APP and user’s historical activities, Grab predicts fraudulent behaviours (chargeback fraud, etc.) to intercept fraudulent bookings when user is ordering;
Leverage explainable AI (e.g. SHAP) to explain this black-box model's prediction, to inform business team or blocked users the reason of block.

SQLPython (Programming Language)Data Analysis

Dbs bank

Data Science Intern

May 2022 – Aug 2022 · 3 mos · Singapore, Singapore

Migrated an ETL pipeline to DBS's Spark cluster on AWS, which is to construct a capital flow network of DBS's clients, using Alluxio, Eclipse Theia, and Pyspark on TB of data;
Analysed the data pipeline to seek and optimize its performance bottlenecks, including substituting TF-IDF with LSH to reduce the time of company name matching.

Data AnalysisSQL