Machine Learning Scientist

Location:Hybrid / Central London

Unique opportunity for a motivated simulation & optimisation specialist to join a rapidly growing and diverse tech start-up. They combine distributed computing, simulation technology and machine learning to develop an autonomous system that’s being adopted by some of the biggest public transport networks in Europe. Now looking for a creative ML Scientist to help roll out this system to more clients.

This a central role in a very exciting and ambitious product development phase where you will be building prediction, simulation and optimisation-based software products. You’ll have the opportunity to drive the choice of technologies used, transforming applications and experimenting with the best new tools for the job based on client needs and industry trends. You will share responsibility for software development, integration and deployment.

You’ll join a team of urban modelling experts, software engineers and data scientists, driven by a shared desire to improve the world around them, to bring something impactful and unique to market and to create a rewarding, fun, working environment.

Requirements

Proficiency in Python and its scientific stack
Mathematical approach: able to understand & develop complex statistical and computational modelling techniques
Bachelor’s degree in a quant field (maths, physics, statistics, computer science, data science, machine learning, operations research, etc.)

Nice to have

Knowledge of database concepts and SQL, or keen to learn
Experience using cloud computing resources; GCP would be ideal

Benefits

Competitive salary + generous benefits package
Join an early-stage start-up at this exciting and critical time when you can help shape the future of the company
In-house and external training opportunities and genuine career development; healthy work-life balance with remote-friendly workplace

Whilst we carefully review all applications, to all jobs, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.