Data Science Intern (6 Months) - Recommendation Engine for Emissions Reduction

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

We are a global technology company, driving energy innovation for a balanced planet.

At SLB we create amazing technology that unlocks access to energy for the benefit of all. That is our purpose. As innovators, that’s been our mission for 100 years. We are facing the world’s greatest balancing act- how to simultaneously reduce emissions and meet the world’s growing energy demands. We’re working on that answer. Every day, a step closer.

Our collective future depends on decarbonizing the fossil fuel industry, while innovating a new energy landscape. It’s what drives us. Ensuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.

Location: Abingdon, UK

Job Summary:

In the emissions reduction sphere, there are many methods to detect, measure and repair leaks. We would like to explore the field of machine arguing in order to compute reduction plans and explanations for emissions reduction based on the flexible constraints. This is a data science focussed internship.

Essential Responsibilities and Duties:

Investigate the domain side of emissions reductions, Investigate the field of machine arguing to create explainable plans using Data Science. Scope for serving the results in a micro frontend application. Will learn about data science and micro frontends.

Qualifications:

Penultimate or final year student, studying towards Bachelors or Masters in Computer Science, Data Science or related field.

Competencies:

Required skills:

- Passionate interest in programming

- Willingness to learn

- Good communication skills

Experience in one of the following would be advantageous

- Machine Arguing

BlueFlex:

We are open to flexible, hybrid working with a combination of on-site & home working days.