Senior Data Scientist (MLOps)

City of London
2 weeks ago
Create job alert

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

Related Jobs

View all jobs

Senior Data Scientist (MLOps)

Senior Data Engineer

Pricing Manager

Pricing Manager - Stratford - Remote - £70k - £85k

Statistician

Biomedical Data Governance Lead

Get the latest insights and jobs direct. Sign up for our newsletter.

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 Apprenticeships: Your In-Depth Guide to a High-Growth Tech Career

Data sits at the heart of modern business. From predictive analytics in finance to personalised recommendations in e-commerce, virtually every sector relies on data to make informed decisions, optimise operations, and drive innovation. Yet, as data volumes grow exponentially, so does the complexity of managing and utilising it effectively. In this evolving environment, data engineering has emerged as a critical discipline—responsible for designing, building, and maintaining the pipelines that transform raw information into actionable insights. If you’re looking to break into this vibrant and future-oriented field, data engineering apprenticeships offer a hands-on, cost-effective route. Designed to fast-track your skills, apprenticeships combine formal training with practical, on-the-job learning. Whether you’re a school leaver or a professional seeking a career change, this guide will walk you through everything you need to know about data engineering apprenticeships in the UK—from the roles you might undertake, to the skills you’ll master, and the exciting career paths that await.

Tips for Staying Inspired: How Data Engineering Pros Fuel Creativity and Innovation

Data engineering stands at the core of modern business intelligence, analytics, and machine learning initiatives. As more organisations become data-driven, the demands on data engineers—everything from building robust pipelines and optimising data warehouses to cleaning and transforming petabytes of raw information—only intensify. Yet, remaining innovative and creative in this rapidly evolving space can be challenging when faced with routine maintenance, endless transformations, and the pressure of meeting tight deadlines. So, how do data engineers stay inspired and consistently generate new ideas? Below are ten actionable strategies to help data pipeline experts, ETL developers, and cloud data architects maintain an inventive outlook, even when operations are complex and the stakes are high. If you’re looking to expand your skills, tackle challenges from fresh angles, and reinvigorate your passion for data engineering, these tips can guide you toward a more fulfilling and impactful career.

Top 10 Data Engineering Career Myths Debunked: Key Facts for Aspiring Professionals

Data is the lifeblood of modern businesses. Whether it’s guiding strategic decisions, powering advanced analytics, or fuelling machine learning models, the role of data has evolved from a back-office function to a primary driver of innovation. In this ecosystem, data engineers serve as architects and builders, designing the infrastructure and pipelines that allow organisations to collect, transform, and mobilise data efficiently. Despite the importance and rapid growth of this field, plenty of myths and misconceptions continue to cloud the realm of data engineering. Are data engineers merely “ETL developers”? Does the role only exist in big tech companies? Must you be a Python guru with a master’s degree in computer science? At DataEngineeringJobs.co.uk, we see firsthand how these myths can deter aspiring professionals from stepping into one of the most dynamic fields in data. This article aims to dispel the top 10 misconceptions about data engineering careers—shedding light on the real opportunities, necessary skills, and diverse pathways that define this vital profession. Whether you’re a student considering data engineering as your future vocation or a seasoned professional seeking a career pivot, read on. You might discover that data engineering is more inclusive and wide-ranging than you ever imagined.