Shilin Wang is a highly experienced data scientist, statistician, and mathematician. Wang has over five years of experience in data mining, machine learning, deep learning, statistical analysis, predictive modeling, Bayesian methodology, graph theory, and combinatorics. She has demonstrated her expertise in the industry, where she has worked on projects such as business loss forecast models, collection models, CECL models, sales forecast models, payment forecast models, balance models, stress testing, credit risk, credit card fraud detection, scoring modeling, credit analytics, reject inference, and more.

Wang is proficient in Python, R, SQL, SAS, Keras, TensorFlow, Jupyter, Spark, Hive table, sas e-miner, and Tableau. She is also skilled in daily use of R-studio, Jupyter, SAS server, Linux, PostgreSQL, and Pyspark. Her skills include various models such as linear/non-linear regression modeling, A/B testing, gradient boosting, neural network, XGboost, random forest, support vector machine, cluster analysis, time series, PCA, feature engineering, survival analysis, weight of evidence, information value, Markov Chain, LDA, QDA, and Bayesian methodology.

Wang has a solid educational background, having completed a Doctor of Philosophy in Mathematics and a Master's degree in Applied Statistics from Louisiana State University. Additionally, she holds a Master's degree in Mathematics from Louisiana State University, a Master's degree in Operational Research and Cybernetics from South China Normal University, and a Bachelor's degree in Major Mathematics & Applied Mathematics; Minor English from South China Normal University.

Wang is currently working as a Senior Analyst at Synchrony and has previously worked as a Data Scientist at Curacao. She has also held roles as a Consultant/Intern and Research Assistant at LSU and South China Normal University.