Alex Wang
AI Freelancer at Chuanxilu.com
Bound Brook, New Jersey, China
Summary
🤩
Rockstar🎓
Top SchoolAlex Wang is an experienced data scientist and analytics leader with 11 years delivering data-driven products and operational strategies across logistics, marketing, and enterprise platforms. He has a strong track record at Yunmanman, where he launched a 0-to-1 enterprise fleet product, built predictive models with ~88% accuracy, and improved order completion via recommendation algorithms. Equally comfortable leading teams and shipping ML, Alex contributes to open-source NLP work—adding CNN and BERT-based architectures to the Kashgari framework—demonstrating hands-on deep learning expertise. Fluent in English and trained in mathematics and business (MBA), he combines quantitative rigor, cross-functional influence, and a knack for turning behavioral signals into product metrics that boost retention and efficiency.
11 years of coding experience
14 years of employment as a software developer
Master of Business Administration (MBA), Business Administration and Management, General, Master of Business Administration (MBA), Business Administration and Management, General at Nanjing University
Chinese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
sequence-labeling10
mask-rcnn10
keras10
faster-rcnn10
transfer-learning10
machine-learning10
rnn-model10
nlp10
deep-learning10
tensorflow10
n10
text-classification10
natural-language-processing10
bert10
gpt5
Programming languages (12)
Github contributions (5)
BrikerMan/Kashgari
Feb 2019 - Jul 2019
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
Role in this project:
ML Engineer
ML EngineerContributions:46 commits, 24 PRs, 1 push in 4 months
Contributions summary:Alex primarily focused on modifying and adding new deep learning models for text classification and sequence labeling tasks within the Kashgari framework. The commits involve the implementation of new model architectures such as AVCNN, KMaxCNN, R-CNN, and DPCNN, along with updates to existing models like BiLSTM and BLSTMCRF. These changes also include optimizing the BERT embedding layer for feature extraction and addressing related unit test modifications to adapt to changes.
bertgptnatural-language-processingtext-classificationtext-labeling
alexwwang/Kashgari
Feb 2019 - Dec 2019
Simple, Keras-powered multilingual NLP framework, allows you to build your models in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks. Includes BERT and word2vec embedding.
Contributions:2 PRs, 50 pushes, 2 branches in 9 months
bertkerasnamed-entity-recognitionnatural-language-processingpart-of-speech
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