Environmental Data Scientist/Hydrologist

Penguin Recruitment
Oxford
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Data Scientist

Senior Data Scientist

Job Title: Senior Environmental Data Scientist/Hydrologist

Ref. No.: CJD1001S26

Location: Based near Oxford

Salary: £35,000 - £42,000

This is a wonderful opportunity to join my client, a highly-regarded, environmentally-savvy Multidisciplinary Consultancy, renowned for lending their expertise to projects across the Water, Transport, and Renewable Energy Sectors. They are currently seeking a talented, enthusiastic Senior Environmental Data Scientist/Hydrologist with demonstrable knowledge of hydrology and hydrological modelling, who is keen to lead a team through their delivery of several challenging projects. You will be based near the beautiful, academic city of Oxford.

Benefits for the role of Senior Environmental Data Scientist/Hydrologist include (but are not limited to):

Competitive salary, rising with experience
Employee Pension Scheme
Very generous annual leave entitlement
A focus on work-life balance, with possibilities of flexible/hybrid working
Healthcare plan
Dedication to your Continuing Professional Development (CPD), with excellent career progression opportunities
Delivery of a wide range of exciting engineering projects across the local region and beyond

Responsibilities for the role of Senior Environmental Data Scientist/Hydrologist include:

Contribute to the development of models and methods, utilising various software platforms, including Qube, CERF, FEH Flood Modelling Suite, ReFH2, and W...

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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.