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I am Jinxin Dong, Ph.D., a data scientist with 3+ years of hands-on experience applying data analysis, statistical modeling, and machine learning to transform data into actionable business insights. I bring strong quantitative and programming skills and a track record of leading cross-functional projects to deliver data-driven innovations and decisions. My research spans environmental engineering and materials science, where I built data-driven tools, knowledge graphs, and ML pipelines for real-world impact. I currently apply ML and NLP techniques at Wuhan Documentation and Information Center, and previously conducted postdoctoral research at Concordia University. I am comfortable turning ambiguous problems into robust, scalable solutions and communicating findings to technical and non-technical stakeholders.…I am Jinxin Dong, Ph.D., a data scientist with 3+ years of hands-on experience applying data analysis, statistical modeling, and machine learning to transform data into actionable business insights. I bring strong quantitative and programming skills and a track record of leading cross-functional projects to deliver data-driven innovations and decisions. My research spans environmental engineering and materials science, where I built data-driven tools, knowledge graphs, and ML pipelines for real-world impact. I currently apply ML and NLP techniques at Wuhan Documentation and Information Center, and previously conducted postdoctoral research at Concordia University. I am comfortable turning ambiguous problems into robust, scalable solutions and communicating findings to technical and non-technical stakeholders.

Jinxin Dong

Data Scientist, AI Engineer, Full Stack Developer, +2

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I am Jinxin Dong, Ph.D., a data scientist with 3+ years of hands-on experience applying data analysis, statistical modeling, and machine learning to transform data into actionable business insights. I bring strong quantitative and programming skills and a track record of leading cross-functional projects to deliver data-driven innovations and decisions. My research spans environmental engineering and materials science, where I built data-driven tools, knowledge graphs, and ML pipelines for real-world impact. I currently apply ML and NLP techniques at Wuhan Documentation and Information Center, and previously conducted postdoctoral research at Concordia University. I am comfortable turning ambiguous problems into robust, scalable solutions and communicating findings to technical and non-technical stakeholders.…I am Jinxin Dong, Ph.D., a data scientist with 3+ years of hands-on experience applying data analysis, statistical modeling, and machine learning to transform data into actionable business insights. I bring strong quantitative and programming skills and a track record of leading cross-functional projects to deliver data-driven innovations and decisions. My research spans environmental engineering and materials science, where I built data-driven tools, knowledge graphs, and ML pipelines for real-world impact. I currently apply ML and NLP techniques at Wuhan Documentation and Information Center, and previously conducted postdoctoral research at Concordia University. I am comfortable turning ambiguous problems into robust, scalable solutions and communicating findings to technical and non-technical stakeholders.

Available to hire

I am Jinxin Dong, Ph.D., a data scientist with 3+ years of hands-on experience applying data analysis, statistical modeling, and machine learning to transform data into actionable business insights. I bring strong quantitative and programming skills and a track record of leading cross-functional projects to deliver data-driven innovations and decisions.

My research spans environmental engineering and materials science, where I built data-driven tools, knowledge graphs, and ML pipelines for real-world impact. I currently apply ML and NLP techniques at Wuhan Documentation and Information Center, and previously conducted postdoctoral research at Concordia University. I am comfortable turning ambiguous problems into robust, scalable solutions and communicating findings to technical and non-technical stakeholders.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Data Scientist, Research Assistant at Wuhan Documentation and Information Center

June 1, 2022 - Present

To automatically identify cutting-edge studies in material field from publications, established a publication identifier based on a binary classification model. Used Crawl4ai to collect 10K+ publications from multi-source; Combined historical and human annotated data to conduct EDA and feature engineering; Trained and optimized classification models, including Random Forest, XGBoost, SVM, and BERT. Identified BERT-based model as the best model with a recall score of 0.90. It reduced annual operational cost by 80%, which is equivalent to $80K per year. Material Property Predictor: Word Embedding Fusion with Knowledge Graph & R-GCN for Performance Forecasting To address inefficiencies of traditional trial-and-error methods in high-temperature superconducting material development, developed a hybrid model integrating Word Embedding, Knowledge Graph, and deep learning modules. Collected entities and relations from 30K+ publications through NER and RE and established Knowledge Graph; genera

Data Scientist Fellow at Techlent Inc.

February 1, 2025 - August 1, 2025

Heart Attack Detector To support early detection and medical intervention, developed a ML pipeline to classify heart disease risks based on basic demographic and clinical characteristics. Performed exploratory data analysis and feature engineering on clinical datasets. Trained and evaluated models including Logistic Regression, Random Forest, XGBoost, and SVM. The XGBoost model achieved an recall of 0.92 for high-risk cases, significantly improving early risk identification. Deployed the solution as a Flask API on GCP for scalable access.

Postdoctoral Researcher, Data Scientist at Concordia University Montreal, Canada

January 1, 2021 - April 1, 2022

Groundwater Contaminant Transport Simulation Software Led the development of a data-driven tool to address groundwater contaminant transport prediction, which replaces costly commercial software. Built the simulation system using Python (core data science tooling) alongside PyQt5 for user-friendly interface; cleaned and integrated 20+ high-impact parameters (e.g., aquifer porosity, groundwater velocity, historical contaminant levels) via KNN imputation; translated environmental data into PDE-solving workflows to enable spatial prediction of contaminant spread. Validated against real-world benchmarks, achieving 92% prediction accuracy vs. commercial alternatives. Delivered $12K in annual cost savings for users while enabling actionable, data-backed contaminant risk insights.

Sales Forecaster at Independent Project

January 1, 2022 - April 1, 2022

To optimize inventory turnover and reduce capital occupation, developed a sales forecaster to predict future demand. Collected relevant data including order date, customer segment, state, region, product category, etc.; Converted categorical data to numerical values; Trained multiple regression models (Linear Regression, Random Forest, AdaBoost, SARIMA, etc.). AdaBoost model demonstrated best performance, with an R² score of 0.77. After deploying the model, inventory costs were reduced by 40%, equivalent to $20K per year.