Environmental Data Scientist / Hydrologist

Advance TRS
London
1 month ago
Applications closed

Related Jobs

View all jobs

Actuarial Data Scientist

Actuarial Data Scientist

Portfolio Revenue & Debt Data Scientist

Portfolio Revenue & Debt Data Scientist - Swindon, Swindon

Junior Data scientist

Senior Data Scientist and Machine Learning Researcher

Environmental Data Scientist / Hydrologist
Location: Wallingford, Oxfordshire
Level: Consultant / Senior Consultant
Salary: £35,000 - £42,000
Hours: Full-time (part-time considered)
Application Deadline: 9 January 2026
About the Organisation
Our client is a specialist UK consultancy operating at the intersection of hydrology, environmental science, and software development. Established in the early 2000s, the business is nationally recognised for developing industry-standard hydrological and flood estimation tools used widely by regulators, consultants, and practitioners across the UK.
The company combines scientific research with commercial software development to deliver cost-effective, sustainable solutions for water resources, flooding, and climate change. As an employee-owned organisation, it offers a supportive, dynamic working environment with a strong focus on professional development, collaboration, and shared success.

The Role

The successful candidate will join the organisation's software development and science team, playing a key role in the development and ongoing management of national hydrological modelling tools and methods.
You will work on software that underpins UK national design standards, addressing real-world challenges including river seasonality, flood mitigation, and climate resilience. The initial focus of the role will be on a national water resources modelling platform, with opportunities to contribute across a broader flood modelling software suite.
A key element of the role will involve applying machine learning techniques alongside established hydrological models to enhance performance, insight, and innovation.

Key Responsibilities

Develop and manage hydrological methods within a national water resource modelling platform.
Contribute to the development and enhancement of UK flood estimation and modelling tools.
Identify and implement machine learning approaches to support and extend existing hydrological models.
Translate scientific research into robust, user-focused commercial software solutions.
Support software testing, validation, and user interface development.
Engage with regulators and end users to ensure compliance, credibility, and usability of tools.

Skills & Experience Required

A good first degree (2:1 or above) in a numerate discipline such as Hydrology, Environmental Science, Civil Engineering, or similar.
A postgraduate qualification is desirable but not essential.
Strong programming skills in Python and/or R.
Practical experience developing and applying machine learning models to environmental or hydrological data.
Ability to work with complex spatial and temporal datasets (e.g. NetCDF, ASCII formats).
Strong written and verbal communication skills, with the ability to engage both technical and non-technical audiences.
Demonstrable experience in hydrology or water-related environmental science.

Your First Year

During your first 12 months, you can expect to:
Build a strong understanding of the organisation's hydrological and flood modelling software tools.
Develop Python modules and apply machine learning techniques to real-world hydrological challenges.
Gain exposure to the UK regulatory framework for water and environmental management.
Collaborate with regulators and leading UK research bodies.
Produce high-quality technical reports and documentation.
Progress towards professional chartership (e.g. CIWEM).

Longer-Term Progression

Beyond the first year, you will have opportunities to:
Contribute to the scientific and commercial strategy of the organisation's software products.
Identify opportunities to expand technical capability and diversify services.
Act as Project Manager on research and development initiatives.
Support client proposals and business development activities.
Contribute to strategic marketing and product positioning.

Benefits & Working Culture

The organisation offers a highly competitive benefits package, including:
Employee ownership benefits, including tax-free profit-share bonuses and performance-related bonuses.
Structured pay scales with clear promotion pathways.
Generous annual leave allowance (40+ days, with buy/sell options).
Employer-matched pension contributions.
Health and wellbeing support, including a healthcare cashback scheme and virtual GP access.
Dedicated annual allowance for training, CPD, and professional memberships.
Flexible working hours and strong IT infrastructure.
Regular staff social events and team-building activities.
A unique office location in a tranquil riverside business park with excellent on-site facilities and transport links.
We are an equal opportunity employer and value diversity in our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

TPBN1_UKTJ

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.