Data Scientist AI Machine Learning, Graduate, Internship, Student Placement

Role Overview & Responsibilities

Are you interested in joining a deep tech company working with leading Aerospace, Defence and Automotive companies like Airbus, GKN, BAE Systems, Nissan Motors etc.?

Are you interested in being part of a young and fun-loving team environment developing cutting-edge AI technology to solve real industrial problems?

If the above questions excite you, this company could be right for you!

We are looking for an engineer passionate about problem-solving. Your design, code, and natural smartness will contribute to solving some of the most complex industrial challenges. You will be involved in shaping our AI products and lead mission-critical projects early in your career.

Key Job Responsibilities

• Develop supervised and unsupervised machine learning models, for example, XGBoost, KNN etc.
• Develop Explainable AI modules to present machine learning results to end users
• Implement multi-objective optimisation algorithms using the trained machine learning (surrogate) models
• Develop uncertainty quantification modules to highlight the uncertainty in the prediction
• Develop deep learning models using Neural Networks, e.g. Auto Encoders, Generative Adversarial Networks (GANs), Federated Learning etc.
• Implement cutting-edge AI algorithms from published scientific documents
• Write technical articles/blogs on industrial use cases.

Qualifications

• Studied Data science, Artificial Intelligence, Machine Learning etc.
• Mastery of Python programming language
• Strong written and verbal communication skills
• Basic working experience/knowledge in Unix/Linux environment
• Basic understanding of containerised applications, for example, Docker
• Basic knowledge of GPU-based highly parallel software development

Job Types: Full-time, Permanent, Graduate, Internship

Benefits:

• Casual dress
• Company events
• Company pension
• Referral programme
• Work from home

Schedule:

• Monday to Friday

Education:

• Master's (preferred)

Work Location: Hybrid remote in Bristol