2 of 2 Data Science Jobs in the City of Westminster

Trainee Data Scientist - No Experience Required Are you looking to kick-start a new career as a Data Scientist We are recruiting for companies who are looking to employ our Data Science Traineeship graduates to keep up with their growth. The best part is you will not need any previous experience as full training will be … pace that suits you.The traineeship is completed in 4 easy steps, you can be placed into your first role in as little as 6-12 months: Step 1 - Full Data Science Career Training You will begin your data science journey by studying a selection of industry-recognized courses that will take you from beginner level all … the way through to being qualified to work in a junior Data Scientist role. Through the interactive courses, you will gain knowledge in Python, R, Machine Learning, AI , and much more. You will also complete mini projects to gain practical experience and test your skills while you study. Step 2 - CompTIA Data+ CompTIA Data+ is an early-career data More ❯

We believe in better. And we make it happen. Better content. Better products. And better careers. Working in Tech, Product or Data at Sky is about building the next and the new. From broadband to broadcast, streaming to mobile, SkyQ to Sky Glass, we never stand still. We optimise and innovate. We turn big ideas into the products, content … Lead Machine Learning Engineer to advance our personalised recommendation systems by developing efficient, low-latency solutions that serve millions of users globally. The successful candidate will collaborate closely with data scientists, engineers, and product managers to design intelligent content recommendation mechanisms and drive the ongoing advancement of our Machine Learning Platform. Model Development: Design, train, and optimise machine learning … models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis. Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. More ❯