D&I Data Scientist

SLB; Kuala Lumpur; On-site; MYR 120,000 - 210,000; Full time

Job summary

SLB in Kuala Lumpur seeks a Data Scientist to tackle open-ended data problems and invent new algorithms for industrial diagnostics. The role requires an advanced degree in a quantitative field and 3–12 years of experience in ML, statistics, and data-driven modeling.

You will work on time-series forecasting, anomaly detection, and AI/ML deployment across enterprise-scale systems, leveraging GenAI/LLMs and cloud-enabled data pipelines.

Qualifications

• 3–12 years of experience in data science, ML, or related fields.
• Strong background in statistical analysis and machine learning techniques.
• Ability to formulate and solve industrial data problems using advanced algorithms.

Responsibilities

• Research and assess next-generation technologies for diagnostics and data-driven optimization of complex systems.
• Demonstrate advanced ML knowledge and population-based optimization methods.
• Generate innovative ideas and shape technical projects from concept to deployment.
• Maintain state-of-the-art knowledge and contribute to technical discussions.
• Apply theoretical knowledge to solve industrial problems.
• Process large multivariate data from equipment operations and tests.
• Develop data-driven algorithms for anomaly detection and failure prediction.
• Communicate ideas and results effectively via reports.
• Collaborate with field and product engineers to identify health monitoring parameters.

Skills

Python

ML Ops

DevOps

Version control

Explainable AI

Time-series

Bayesian techniques

NLP-GenAI

Education

Advanced degree in quantitative field

Tools

NumPy

pandas

scikit-learn

Keras

TensorFlow

PyTorch

Job description

The Data Scientist is responsible for conducting undirected research and tackle open-ended data problems and questions. Drawing on an advanced degree in a quantitative field such as computer science, physics, statistics or applied mathematics, the Data Scientist demonstrates the knowledge to invent new algorithms to solve data problems.

Responsibilities

• Research and assess next-generation technologies for machinery diagnostics and prognostics and data-driven modeling and optimization of complex systems.
• Demonstrate advanced working knowledge and experience with machine learning algorithms and population-based meta-heuristic optimization methods.
• Generate innovative ideas, establish new research directions, and shape and execute on technical projects.
• Maintain state-of-the-art knowledge and contribute to technical discussions and reviews as an expert in related areas of responsibility.
• Apply theoretical knowledge to solve industrial problems.
• Process large multivariate data sets collected from equipment operations, manufacturing tests and diagnostic routines.
• Apply engineering knowledge in developing data-driven algorithms for anomaly detection, failure prediction and optimization.
• Communicate ideas, plans and results effectively via oral and written reports.
• Collaborate with field and product engineers to identify key health monitoring parameters of a system.
  

Experience level: 3-12 years

Expertise

• Knowledge and practical experience in statistical analysis techniques (e.g., classification, regression, time-series, Bayesian techniques) and machine learning techniques (e.g., decision trees, ensemble methods, deep learning, neural networks, validation methods).
• Practical experience in the machine learning lifecycle, from problem formulation and data acquisition to model building and deployment at enterprise scale.
• Conceptual and mathematical understanding of algorithms, models, model assessment techniques, solution development and explainable AI.
• Knowledge and experience in code design, testing, and ML Ops practices.
• Specialization in at least one sub-domain, such as time series forecasting, Bayesian skills or NLP-GenAI.
• Proficiency in Python, including packages such as NumPy, pandas, scikit-learn, Keras, TensorFlow, and PyTorch.
• Experience with software engineering practices, agile methodologies, DevOps, and version control.
• Experience in Data and Cloud environments along with APIs and data integration
• Experience with GenAI/LLMs