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 (User Modelling/Representation Learning) - Viator

Senior Machine Learning Engineer, NLP

Senior Machine Learning Engineer

Senior Machine Learning Engineer - ML Infrastructure

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.

Neurodiversity in AI Careers: Turning Different Thinking into a Superpower

The AI industry moves quickly, breaks rules & rewards people who see the world differently. That makes it a natural home for many neurodivergent people – including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a career in artificial intelligence, you might have been told your brain is “too much”, “too scattered” or “too different” for a technical field. In reality, many of the strengths that come with ADHD, autism & dyslexia map beautifully onto AI work – from spotting patterns in data to creative problem-solving & deep focus. This guide is written for AI job seekers in the UK. We’ll explore: What neurodiversity means in an AI context How ADHD, autism & dyslexia strengths match specific AI roles Practical workplace adjustments you can ask for under UK law How to talk about your neurodivergence during applications & interviews By the end, you’ll have a clearer picture of where you might thrive in AI – & how to set yourself up for success.

AI Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we head into 2026, the AI hiring market in the UK is going through one of its biggest shake-ups yet. Economic conditions are still tight, some employers are cutting headcount, & AI itself is automating whole chunks of work. At the same time, demand for strong AI talent is still rising, salaries for in-demand skills remain high, & new roles are emerging around AI safety, governance & automation. Whether you are an AI job seeker planning your next move or a recruiter trying to build teams in a volatile market, understanding the key AI hiring trends for 2026 will help you stay ahead. This guide breaks down the most important trends to watch, what they mean in practice, & how to adapt – with practical actions for both candidates & hiring teams.

How to Write an AI CV that Beats ATS (UK examples)

Writing an AI CV for the UK market is about clarity, credibility, and alignment. Recruiters spend seconds scanning the top third of your CV, while Applicant Tracking Systems (ATS) check for relevant skills & recent impact. Your goal is to make both happy without gimmicks: plain structure, sharp evidence, and links that prove you can ship to production. This guide shows you exactly how to do that. You’ll get a clean CV anatomy, a phrase bank for measurable bullets, GitHub & portfolio tips, and three copy-ready UK examples (junior, mid, research). Paste the structure, replace the details, and tailor to each job ad.