About me

I’m an applied scientist with the engineering and statistics background and with the great passion about using Machine learning and Operations Research to drive business insights. Over the past years, I’ve developed a strong skill set in statistical, operations research, simulation, dynamic programming, time series forecasting and machine learning models all of which fall under the umbrella of advanced analytics. Having a PhD degree with the focus in Analytics helped me gain tremendous analytical & problem-solving skills and most importantly a lifelong skill of learning how to learn. That’s why, I’m always keen to learn new tools and techniques whenever necessary to stay up to date with the industry needs. At the moment, I’m eager to use analytics (i.e., predictive, prescriptive and descriptive) methods to solve complex real-life problems in finance, information technology and healthcare.

Skillset:

■ Programming: Python (e.g., scientific computing stack) R, Java, SQL

■ Operations Research/Optimization: Linear Programming, Mixed Integer Programming (Gurobi, CPLEX), Markov Decision Processes, Approximate Dynamic Programming, Multi-Criteria Decision Analysis

■ Machine Learning (scikit-learn), Deep Learning (Keras)

■ Time Series Forecasting Analysis (fpp3, statsmodels)

■ Data Visualization (Tableau, Plotly, Seaborn, Matplotlib)

■ Survival Analysis: Parametric (e.g., Weibull) and Non-Parametric (Kaplan-Meier) Analysis

■ Version Control: Git/GitHub

■ Cloud/Distributed Computing: Azure, Databricks

Fun Fact: I love nature, and thus, I love playing soccer and outdoor activities such as kayaking and canoeing.