Tony Dong
Analytics & Innovation Manager, AuthAI
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolTony Dong is an Analytics and Innovation leader with 8 years of experience building production ML and forecasting systems for supply chain, healthcare, and enterprise products. He has driven measurable operations improvements—such as a 37% forecast accuracy gain, 19% workforce efficiency uplift, and a 21% improvement in international ETA estimation—by combining time-series, ML models (XGBoost, LSTM, SARIMAX), Monte Carlo simulation, and scalable data pipelines. Comfortable across Google Cloud, BigQuery, Spark, Python, and Looker, Tony translates business needs into end-to-end analytics products and automated reporting for cross-functional teams. His background in engineering and an MBA give him a rare ability to marry technical rigor with strategic measurement and stakeholder communication. At Availity he now leads AuthAI reporting and partnerships, continuing to prototype innovations with minimal direction and an emphasis on operationalizing impact.
8 years of coding experience
11 years of employment as a software developer
Master of Business Administration - MBA, Master of Business Administration - MBA at University of Connecticut School of BusinessMaster of Engineering - MEng Robotics Technology/Technician, Master of Engineering - MEng Robotics Technology/Technician at Peking UniversityBachelor's degree Mechatronics Robotics and Automation Engineering, Bachelor's degree Mechatronics Robotics and Automation Engineering at Northeastern University (CN)
English, Chinese
Github Skills (14)
traversal10
automated-machine-learning10
encoding10
supervised-learning10
feature-selection10
preprocessor10
automl9
ensemble-learning9
pipeline9
feature-engineering9
scalable9
machine-learning8
python8
data-science7
Programming languages (1)
Github contributions (5)
tonyleidong/OptimalFlow
Jul 2020 - Mar 2022
OptimalFlow is an omni-ensemble and scalable automated machine learning Python toolkit, which uses Pipeline Cluster Traversal Experiments(PCTE) and Selection-based Feature Preprocessor with Ensemble Encoding(SPEE), to help data scientists build optimal models, and automate supervised learning workflow with simpler coding.
Contributions:371 commits, 182 pushes, 2 branches in 1 year 8 months
scientistspythonfeature-basedensembleensemble-encoder
tonyleidong/DynamicPipeline
Jul 2020 - Aug 2020
Contributions:137 pushes, 1 branch in 19 days
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