Senior Environmental Data Scientist/Hydrologist

Focus Resourcing
Wallingford
2 months 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

Senior Data Scientist and Machine Learning Researcher

Senior Data Scientist

Location: Wallingford, UK (Remote considered)
Hours: Full-time (part-time considered)
Closing Date: 9 January 2026

Shape the future of hydrology and climate resilience. Our client is offering an exciting opportunity for an ambitious, collaborative Environmental Data Scientist or Hydrologist to join our clients growing software development team in Wallingford. If you want to innovate, solve real-world water challenges, and influence national environmental tools, we’d love to hear from you. In this role you will play a key role in developing our hydrological methods, modelling tools, and national design-standard software. Working at the intersection of hydrology, data science, and software development, you’ll contribute to new methodologies, develop machine learning approaches, and support the scientific foundations of our products.

You'll help advance the science powering products such as:

  • Qube - our clients online water resources modelling platform, incorporating the CERF rainfall-runoff model.
  • FEH Flood Modelling Suite - ReFH2 and WINFAP5, the UK’s trusted flood estimation tools.
Your role
  • Develop and manage hydrological methods for Qube.
  • Contribute to ReFH2 and WINFAP5 development.
  • Explore and implement machine learning enhancements to hydrological models.
  • Support scientific research and integrate findings into commercial software.
  • Work closely with regulators and users to ensure compliance, quality, and usability.
Required Skills & Experience
  • A good degree (2:1+) in a numerate discipline (Hydrology, Environmental Science, Civil Engineering, etc.).
  • Strong programming skills in Python and/or R.
  • Experience developing machine learning models for environmental or complex datasets.
  • Confidence working with spatial/temporal datasets (NetCDF, ASCII, etc.).
  • Excellent communication skills for both technical and non-technical audiences.
  • Demonstrable experience in hydrology or water-related environmental science.
  • A relevant postgraduate qualification is welcome but not essential.
What you can expect in year one
  • Build deep expertise in Qube, CERF, and the FEH flood modelling suite.
  • Develop Python modules and apply ML methods to hydrological problems.
  • Become familiar with UK water environment regulatory frameworks.
  • Collaborate with leading UKCEH scientists and liaise with UK regulators.
  • Produce high-quality technical reports.
  • Begin your journey toward professional chartership (e.g., CIWEM).
Following your first year, opportunities include
  • Influencing the strategic development of our software products.
  • Leading R&D projects as a Project Manager.
  • Helping develop client proposals.
  • Contributing to our strategic marketing and product development plans.
Benefits & Culture
  • 40+ days holiday (with buy/sell options).
  • Profit-share and tax-free bonuses through employee ownership.
  • Matched pension contributions (5-10%).
  • Health plan, Cycle to Work, Environment Day.
  • 5 days training per year, plus support towards chartership.
  • Flexible working arrangements.
  • Financial support for professional memberships.
  • Formal appraisal and personal development planning.
  • Flexible working hours.
  • High-quality IT infrastructure & personal computing budget.
  • Fun annual staff events (axe throwing, escape rooms, and more).


#J-18808-Ljbffr

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.