Yin Song
Senior Applied Scientist at Amazon Web Services (AWS)
Sydney, New South Wales, Australia
Summary
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Senior🎓
Top SchoolYin Song is a Senior Applied Scientist based in Sydney with 10 years of experience applying probabilistic modeling, big data engineering, and optimization to real-world products and platforms. Currently at AWS, Yin has a track record of moving advanced research into production—from transaction marketing optimization at Rokt and Brandscreen to large-scale data science at Telstra and AWS. His background spans academia and industry, including a PhD in Big Data from UTS and early research on speech and pronunciation evaluation at Tsinghua, giving him a strong foundation in statistical learning and signal processing. Comfortable across languages and ecosystems (R, Python, Clojure, C++, Hadoop, Spark, Redshift, S3), he combines functional-programming sensibilities with pragmatic engineering for robust data pipelines and algorithms. Notably, he has built and shipped econometric and decision-optimization systems that directly influenced marketing experience and fraud-detection workflows. He balances deep technical rigor with product impact, thriving on turning complex probabilistic models into scalable, production-ready solutions.
10 years of coding experience
11 years of employment as a software developer
Bachelor, Electrical and Electronics Engineering, 88/100, Bachelor, Electrical and Electronics Engineering, 88/100 at Beijing Normal UniversityDoctor of Philosophy (PhD), Information Technology, Big Data, Doctor of Philosophy (PhD), Information Technology, Big Data at University of Technology, SydneyMaster, Intergrated circuit design, 84.3/100, Master, Intergrated circuit design, 84.3/100 at Tsinghua University
Chinese
Stackoverflow
69reputation
389kreached
2answers
0questions
Github Skills (82)
transformers10
language-model10
python10
gluonts10
flax10
deep-learning10
get-started10
transformer10
nlp10
jax10
seq2seq10
choices10
tensorflow10
natural-language-processing10
cuda10
Programming languages (4)
Github contributions (5)
awslabs/mlmax
Mar 2020 - Feb 2022
Example templates for the delivery of custom ML solutions to production so you can get started quickly without having to make too many design choices.
Contributions:1 review, 166 commits, 12 PRs in 1 year 11 months
get-starteddeep-learninginferencemachine-learningmlops
aws-samples/amazon-sagemaker-forecast-algorithms-benchmark-using-gluonts
Mar 2020 - Jul 2021
This repository contains the sample code to benchmark popular time series forecast algorithms using Gluonts in AWS Sagemaker Notebook Instance.
Contributions:4 commits, 1 PR, 2 pushes in 1 year 4 months
sagemakernotebookmachine-learningforecastbenchmark
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