Graduate Data Scientist

Arcadis
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Graduate Data Scientist Engineer: Build Smart Data Systems

Graduate Data Scientist - Newcastle - Asset Management

Graduate Data Scientist

Graduate Data Scientist

Graduate Data Scientist

Graduate Data Scientist: Energy Markets & Analytics

Arcadis is the world's leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets.

We are more than 36, people, in over 70 countries, dedicated to improving quality of life. Everyone has an important role to play. With the power of many curious minds, together we can solve the worlds most complex challenges and deliver more impact together.

Role description:

Our graduate GROW programme is a structured 3-year programme, designed to help you evolve and develop skills to become an experienced professional in your chosen field. Youll benefit from training and development, mentoring, and assistance foraccreditations relevant to your programme, as well as the support and guidanceyou need to develop and succeed. As a GROW graduate you will be empowered to drive your career and will be exposed to a variety of experiences to assist you in gaining the skills you require to succeed.

As an engineer joining GROW, you will be given the opportunity to specialise within your chosen discipline of engineering and we ensure you obtain the appropriate experiences that you need to become a technical expert. Being an Engineer at Arcadis is about applying a human filter to all your technical expertise and training, and making an impact through innovation and creativity. During this three-year programme you will develop core engineering and consulting techniques that can be applied while working with our clients in the UK and potentially even some of our international clients around the globe. 

Role accountabilities:

The Data Science and Analytics Group form part of the Arcadis Intelligence Global Business Area, whose mission is to drive digital leadership and deliver sustainable digital solutions to out clients, that secure planet-positive, tangible outcomes. There is a significant opportunity to join our group of data experts, advanced analytics solutions engineering and analytics consulting practices, where you will support with descriptive and predictive modelling (including Machine Learning), as well as solving complex mathematical optimisation challenges principally, but not exclusively related to Asset Investment Planning. Our projects sit across multiple sectors and the end-to-end asset life-cycle - including Water, Energy, Cities, Transport and Large Buildings Portfolios.

Accepted Degrees: BSc/BEng/MSc/MEng/MPhys in Mathematics, Statistics, Physics, Chemistry, Environmental Science, Operational Research, Data Science, Civil Engineering, Mechanical Engineering related degree.

Qualifications & Experience:

You will be graduating in or have recently graduated with at least a 2:2 in a relevant master's or bachelor's degree You will demonstrate typical Arcadian skills including Resilience, Creativity, Analytical Thinking and being a Team Player You will have excellent communication skills and the ability to use your technical knowledge to influence and consult stakeholders. Willing to travel and stay away from homewhen required (where possible we embrace virtual working) 

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.