Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 2

• Schlumberger Machine Learning Innovation competition, 2nd runner-up, Apr 2023
• Millenial Energy Challenge – Data Science competition, Champion, Aug 2021
Experience:

Machine Learning and Petroleum Engineer Jan 2023 – Present , Risehill Energy Solution, Malaysia:
• Managed, cleaned, and examined production data to identify trends and patterns using numpy, pandas
• Designed a modern, user-friendly UI for the Production Data Analysis Platform in line with current design principles
and business standards using streamlit, plotly
• Implemented real-time event identification and buildup automation using pressure and rate data
• Developed a fluid production forecasting model through feature engineering and pre-processing of fiber-optic
datasets using principal component analysis
• Employed Recovery Factor Benchmarking to assess compartmentalization and estimate the likelihood of Reserve
Additions from Infill Wells based on production data
• Streamlined data preparation with code that resulted in a 50% increase in processing speed using dask


Personal Projects:
Automated PC Curve Generation by Clustering:
• Using advanced clustering algorithms such as GMM, HDBSCAN, and K-Means, the iterative manual process of
generating Capillary pressure curves utilized in PETRONAS was eliminated. Additionally, the process of matching Original Oil in Place values was shortened by comparing water saturation values from log and Pc curve, as opposed to using dynamic and static models.

Calculation of Production Back Allocation using Machine Learning Algorithms:
• Using machine learning and deep learning algorithms, Multiple Linear, Support Vector Machine, Random Forest,
LSTM to predict the back allocation values

London Taxi Travel Duration dataset:
• The travel durations for taxis were predicted by utilizing a large dataset and tools such as Geopandas, Google Maps API, and OpenStreetMap API. By developing a novel method to quantify traffic, the accuracy score was improved by 30%, leading to a victory in the competition.

Job Board Web Scraping:
• Job Board Data Web Scraping Automation With Python using Selenium

Laptop price predictor:
• In this project I built a website for laptop price prediction. It covers end-to-end machine learning processes including Data preprocessing, Data cleaning, Data visualization, Feature engineering, Modeling, Building a website with Streamlit, and Deploying a website to Herok.