Data Scientist

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. This is an Internship opportunity at SLB Kuwait.

Responsibilities

Research and assess next-generation technologies for machinery diagnostics and prognostics and data-driven modeling and optimization of complex systems.

Analyze raw data: assessing quality, cleansing, structuring for downstream processing Design and deploy visualizations and dashboards using both structured and unstructured data. Deploy Generative AI and Agentic AI solutions.

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.

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.

Required Skills

& Qualifications Fresh Graduates with Bachelor's degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.) Programming: High proficiency in Python (specifically Pandas for data manipulation and NumPy for numerical computing).

Database Management: Ability to write SQL queries to extract data from relational databases. Visualization: Strong experience with Power BI, Angular, or similar technologies for developing data visualizations and interactive dashboards.

Statistical Foundation: Understanding of probability, hypothesis testing, and statistical significance. Machine Learning: Familiarity with ML libraries like Scikit-Learn.

Education: Currently pursuing or recently completed a degree (BS/MS) in Data Science, Statistics, Computer Science, Computer Engineering, Mathematics, Electrical & Electronics Engineering or a related field. Bonus Basic exposure to LLMs or Prompt Engineering (e. g. , using OpenAI or LangChain APIs).