Data Engineer jobs in Malaysia
Data Scientist for Oil & Gas Industry
Mission Consultancy Services
Senior Machine Learning Engineer
GrabTaxi Holdings Pte. Ltd.
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Merck Gruppe - MSD Sharp & Dohme
Senior Data Scientist (FMCG) - Mandarin Speaking
NielsenIQ
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Job summary
A consultancy firm in Kuala Lumpur is seeking an experienced Data Scientist specialized in the Oil & Gas industry. The ideal candidate will have a strong background in Machine Learning and a degree in relevant fields. Responsibilities include developing ML models, data integration, and analyzing trends to support decision-making in subsurface geology. This position is onsite and requires a minimum of 5 years of experience.
Qualifications
- Minimum 5 years experience in Data Science or Statistical Modeling.
- Experience or knowledge of Geo Science.
- Having geoscience knowledge will be an advantage.
Responsibilities
- Develop Machine Learning model using Python.
- Gather and review necessary data from relevant sources.
- Configure data integration from various data sources.
- Perform data quality, cleaning, and data integrity checks.
- Analyze large amounts of information to discover trends.
- Train the Machine Learning model with appropriate algorithms.
- Validate the model by evaluating its performance.
- Perform user acceptance testing to verify integration.
- Configure necessary API for integration of ML model.
- Prepare admin documentation for reference.
Skills
Education
Our Client is looking for an experienced data scientist with a background in the Oil & Gas Industry for the development of Machine Learning (ML) model driven solutions for missed opportunity portfolio ranking and formulating future plan of exploitation including but not limited to ML approaches for HC pay identification at Well and ML approaches for Seismic Scale HC Pay identification.
JOB RESPONSIBILITIES :
- Develop Machine Learning model using Python
- Gather and review necessary data from relevant sources to set up a comprehensive dataset as variables for training the Machine Learning (ML) model
- Configure data integration from various data sources (not limited to well logs, seismic attributes, and production data) to provide a comprehensive understanding of subsurface geology data into a combined dataset for further analysis. This involves merging data, checking data compatibility, and resolving inconsistencies or discrepancies for data frame preparation
- Perform data quality, cleaning, and data integrity checks to resolve any data issues that may affect accuracy and reliability of the model
- Analyze large amounts of information to discover and recognize trends and patterns as part of exploratory data analysis
- Train the Machine Learning model by selecting appropriate algorithms, defining input features and target variable for missed opportunity portfolio ranking prediction analytics solution
- Validate the model by evaluating its performance to study effectiveness and model accuracy. ML algorithms can quantify the uncertainty associated with predictions, providing decision-makers with confidence levels and risk assessments
- Perform user acceptance testing to verify the integration, assess the model's performance and address any feedback from users
- Configure necessary API for integration of ML model with solution user interface
- Prepare admin documentation for reference
- Implementation & configuration for go-live
- Support stabilization period to fix issues and bugs after deployment
- Lead/guide/mentor/train project team members data scientists during the development
- Minimum 5 years experience in Data Science or Statistical Modeling
- Experience or knowledge of Geo Science
- Degree in Computer Science, Physics, or Statistics
- Having geoscience knowledge will be an advantage
- Job is onsite in office
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.