Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Data Scientist (MLOps)

City of London
6 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer - Azure

Data Engineer

Senior Data Engineer

Data Engineer

Data Engineer

Data Engineer

A world class Tech Organisation are looking for a Senior Data Scientist (MLOps) to join their division in London on a hybrid basis - opportunity to join a really innovative environment where you'll work with cutting edge technologies.

The company:

The organisation have been running very successfully now for over twenty years and are recognised as market leaders in their sector. They have a global footprint, and their products are used by millions of users every single day.

They are entering a really exciting period of growth, and are recruiting for a number of new positions to the business as they've got pretty big plans for the next few years - so it's genuinely a great time to join.

They thrive on a positive and welcoming culture making it a great place to work, so it probably comes as no surprise that they have really low attrition rates, as so many of their staff members have long and successful careers with the business.

The role:

You'll be joining a multi-disciplinary Senior squad of roughly 6 consisting of Principle and Senior Software Engineers, Data Engineers and Data Scientists, and will be tasked with supporting machine learning teams with deploying and maintaining models in production, ensuring they are reliable, scalable, and adhere to best practices.

You'll be involved optimizing model performance, mitigating risks, and refining deployment pipelines to meet governance and regulatory standards. You will collaborate with the ML platform team advocating for effective use of tools like feature stores and model registries.

This role acts as the glue between data science and platform engineering teams, fostering MLOps best practices, addressing bottlenecks in inference and retraining pipelines, and resolving production issues to enhance system robustness and cost efficiency.

Key skills and experience:

** Prior Senior Data Scientist with Machine Learning experience

** Strong understanding and experience with ML models and ML observability tools

** Strong Python and SQL experience

** Spark / Apache Airflow

** ML frame work experience (PyTorch / TensorFlow / Scikit-Learn)

** Experience with cloud platforms (preferably AWS)

** Experience with containerisation technologies

Useful information:

Their offices are based in central London where they support hybrid working, you'll be expected onsite about twice a week, however they are really flexible about what days.

They're offering a very competitive salary from £70,000 - £95,000, depending on experience with great benefits to match (which include multiple bonuses and more!).

If you're keen to find out more, please reach out to Matthew MacAlpine at Cathcart Technology

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Engineering Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data engineering hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise reliable pipelines, modern lakehouse/streaming stacks, data contracts & governance, observability, performance/cost discipline & measurable business outcomes. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for platform‑oriented DEs, analytics engineers, streaming specialists, data reliability engineers, DEs supporting AI/ML platforms & data product managers. Who this is for: Data engineers, analytics engineers, streaming engineers, data reliability/SRE, data platform engineers, data product owners, ML/feature‑store engineers & SQL/ELT specialists targeting roles in the UK.

Why Data Engineering Careers in the UK Are Becoming More Multidisciplinary

For many years, data engineering in the UK meant designing pipelines, moving data between systems, and ensuring analysts had what they needed. Today, the field is expanding. With cloud platforms, machine learning, real-time analytics and the explosion of sensitive personal data, employers expect data engineers to do much more. Modern data engineering is no longer just about code and storage. It requires legal awareness, ethical judgement, psychological insight, linguistic clarity and human-centred design. These disciplines shape how data is collected, processed, explained and trusted. In this article, we’ll explore why data engineering careers in the UK are becoming more multidisciplinary, how law, ethics, psychology, linguistics & design now influence job descriptions, and what job-seekers & employers must do to thrive.

Data Engineering Team Structures Explained: Who Does What in a Modern Data Engineering Department

Data has become the lifeblood of modern organisations. Every sector in the UK—finance, healthcare, retail, government, technology—is increasingly relying on insights derived from data to drive decisions, deliver products, and improve operations. But raw data on its own isn’t enough. To make data useful, reliable, secure, and scalable, companies must build strong data engineering teams. If you’re recruiting for data engineering or seeking a role, understanding the structure of such a team and who does what is essential. This article breaks down the typical roles in a modern data engineering department, how they collaborate, required skills and qualifications, expected UK salaries, common challenges, and advice on structuring and growing a data engineering team.