Xin Fang is a Principal Data Scientist with a PhD in Chemical Engineering and over a decade of interdisciplinary experience translating advanced statistical and ML research into business impact across telecom, cloud, and finance at companies like Verizon, Amazon, Fidelity, and Oracle. He combines deep expertise in supervised/unsupervised learning, deep learning, and NLP with hands-on mastery of Python, R, SAS, SQL and big-data platforms (Spark, Hadoop, Flink, Kafka, AWS) to build production predictive and prescriptive analytics. A published researcher with 10+ scientific papers and a background running computational biology simulations, he brings a rare blend of rigorous scientific method and product-driven deployment. Known for leading analytics at scale, he frequently bridges domain science and engineering to turn complex data into actionable decisions for enterprise stakeholders.
9 years of coding experience
16 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Georgia Institute of Technology
PhD, Chemical Engineering, PhD, Chemical Engineering at University of Maryland
BS & MS, Chemical Engineering, BS & MS, Chemical Engineering at Tsinghua University
Graduate Certificate, Data Mining and Applications, Graduate Certificate, Data Mining and Applications at Stanford University
Enhance the Spotify experience with real-time song lyrics and more.
Contributions:107 commits, 104 pushes, 1 branch in 1 year 4 months
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