Senior Machine Learning Scientist

Markerstudy Group
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Engineer

Job title: Senior Machine Learning Scientist

Location: Flexible

Role overview

Markerstudy Group have a very exciting opportunity for a Senior Machine Learning Scientist to support the delivery and deployment of Insurance Claims and Operations use cases.

You will have the technical support of an established machine learning function, to then create fully automated machine learning pipelines.

You will be supported by an Operations Insight function that have vast experience in the delivery, evaluation, and performance tracking of machine learning models.

The role will be working in an exciting, diverse and changeable environment, key stakeholders will be across Broker Services, Customer & Third Party Claims, Counter Fraud and Continuous Improvement.

Responsibilities:

Adhering to best practice, covering all aspects of machine learning, ensuring policies and procedures are adhered to Create robust high-quality code using test-driven development (TDD) techniques and adhering to the SOLID coding standard Deploy and maintain machine learning methods in a DevOps / MLOps based machine learning environment Tune machine learning methods for optimal performance. Deploy and maintain machine learning methods in our machine learning pipelines using robust test-driven development (TDD) coding approaches, using the SOLID software development principles. Actively contribute to creating a culture of coding and data excellence Mentor and coach, a small, specialized team of junior machine learning specialists and insight analysts

Key Skills and Experience:

Experience in tuning and deploying machine learning methods Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets, Clustering, Isolation Forest, SVMs, NLP Experience in DevOps and Azure ML, or other MLOps and ML Lifecycle technology stacks, such as AWS, Databricks, Google Cloud, etc. Experience in creating production grade coding and SOLID programming principles, including test-driven development (TDD) approaches Experience in programming languages (e.g. Python, PySpark, R, SAS, SQL) Experience in source-control software, e.g., GitHub Ability to demonstrate that bias and ethics have been considered throughout the model build and deployment Ability to track model performance including degradation and provide a clear and concise view on explainability Proficient at communicating results in a concise manner both verbally and written

Behaviours:

A high level of professional/academic excellence, educated to at least a master’s level in a STEM-based or DS / ML / AI / or mathematical discipline Collaborative and team player Logical thinker with a professional and positive attitude Passion to innovate and improve processes

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.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.