Higher Scientific Officer

Belfast
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist - Pricing

Data Scientist - Pricing

Environmental Data Scientist/Hydrologist

Environmental Data Scientist/Hydrologist

Postdoctoral Fellow- Computational Biology and Machine Learning

Environmental Data Scientist/Hydrologist in Oxford)

Higher Scientific Officer

Belfast, Northern Ireland

Competitive Salary & Benefits

18-month Fixed Term Contract (end of March 2026) - Full Time Hours

Reed Scientific is working with a leading provider of scientific research and services to a variety of organisations including governmental, non-governmental and commercial businesses, based in Northern Ireland, the rest of the UK as well as some based further afield within the European Union.

This Higher Scientific role will include investigating the impacts of agricultural land use practices on soil, water and air, and identifying ways of optimizing land-based livestock production. You will work as part of a multi-disciplinary team and help contribute to activities such as data collection, processing, analysis, interpretation and write-up of your findings.

Key skills/experience required:

PhD or MRes in an environmental/agricultural science or related field
Experience using/working with software packages related to GIS to process and analyse data relating to land use
Understanding of current freshwater and agricultural pressures in Europe
Proven ability to write scientific reports, analyse big data sets and present your results/findings to key stakeholders
Hold a full driving licenceIf this sounds like a position of interest and you possess the key skills and experience, then please apply via the link

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.

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

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.