• Served as a full-stack data scientist to design, build, and research end-to-end batch processing machine learning pipelines, which included data cleaning, preprocessing, model research, validation, deployment, and monitoring.

• Initiated advanced labeling techniques (statistics,Markov chain, soft labeling, andactive learning) to synthesize default labels for creditrisk model, facilitating faster convergence in small and short-duration datasets. This expedited model launching by achieving a notable 7% improvement in AUC during the early launch phase, crucial for the company's fundraising success and securing a $20 million investment.

• Conducted interviews, actively contributing to the hiring process fostering team growth with stability and high performance.

• Developed a highly scalable and easily deployable Python framework for machine learning research, abstracting various tools, including Scikit-learn, TensorFlow, Optuna, Imbalanced-learn, XGBoost, Seaborn, Matplotlib, and others. Includes composable core ML components for preprocessing, (un)supervised learning,deep learning, and recommendation systems, plus robust error handling capabilities in production. Streamlining research and deployment, it achieves estimated time savings of over 50%, with a flawless deployment record—zero P1 reported incidents in production.

• Cultivatedsecure,scalable,maintainable and reliable codebases through adherence to strict guidelines for infrastructure as code, continuous integration­/­deployment, unit/integral testing, software development­/­coding best practices and security, using modern tools such as Terraform Cloud, CI/CD pipelines, GitHub Actions, code analysis and security­/­vulnerability tools.

• Designed and developed algorithm-driven software using Python, VBA, and linear programming for production optimization, resulting in a 15.2% increase in oil production, $15.3 million/year in additional revenue, and improved workforce efficiency with reduced errors.

• Led the development of a software application using Python and VBA to enable scalable pipeline network modeling, cutting investment costs by $49.8 million.

• Demonstrated exceptional project management skills in coordinating cross-functional teams for various operational projects driving $128.5 million more benefits and zero injury/incident from my projects.