Senior Data Scientist (MLOps)

City of London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Data Engineer

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.

New Data Engineering Employers to Watch in 2026: UK and Global Companies Driving the Data Revolution

Data engineering is at the heart of the digital economy, transforming raw data into actionable insights, powering analytics, AI systems, and cloud infrastructure. As the UK and global markets continue to invest heavily in data platforms, pipelines, and real-time analytics, demand for skilled data engineers is growing rapidly. For professionals exploring opportunities on www.DataEngineeringJobs.co.uk , the critical question is: which companies are expanding, hiring, and shaping the future of data-driven business? This article highlights new data engineering employers to watch in 2026, including UK startups, scale-ups, and international firms expanding in the UK.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.