JBA Graduate Scheme Pathways

Data Science and Informatics

Could your analytical thinking help to unlock resilient solutions?

This pathway is perfect for graduates who are curious, analytical and passionate about solving complex problems. You'll work on a wide variety of projects, combining data science, statistics, modelling, computing and AI to tackle challenges in flooding, climate change and environmental risk.

Supported by experts across JBA, you'll develop innovative solutions, uncover valuable insights and help organisations make informed decisions that create lasting benefits for people, businesses and the environment.

Is this the right pathway for you?

Your degree might be in...

Data Science
Mathematics
Statistics
Computer Science
Physics
Operational Research
Other highly quantitative disciplines

You're interested in...

Machine learning and AI applications
Predictive modelling
Statistical analysis
Climate and flood risk research
Data visualisation
Image analysis
Digital terrain model (DTM) analysis
Rainfall and hydrological analytics
Decision-support tools
Environmental data science projects using languages such as Python and R.

Discover how our graduates are applying their data science skills to real-world projects:

Non-stationarity of UK rainfall frequency

“This was an internal research project aiming to build a knowledge base within JBA and to present findings to the research community. My time spent on this project included undertaking a literature review of the existing methods to apply non-stationarity to rainfall analysis, trend detection on gauging sites across the UK and capturing these trends using a UK-wide generalised extreme value model. Starting with very little knowledge about hydrology, I have been able to learn lots of new skills as well as applying my existing skills in statistics, coding (both in R and Python) and data visualisation.”

Amy Winder, Data Scientist

CoastSnap AI

“This project aimed to build on a previous project by analysing images from a global citizen science project, CoastSnap, using AI methods. I contributed to this project by helping develop a convolutional neural network (U-Net model) for segmenting images from CoastSnap. My focus was on capturing key features, such as the sea wall, using machine learning. Beginning with limited knowledge about image segmentation, acquired new skills such as image processing with machine learning and applied my existing expertise in statistics and coding (Python).”

Roxanne Upton, Data Scientist

Global flood frequency

“This project focused on creating a workflow to generate accurate river flow return periods for any latitude and longitude with an available digital terrain model (DTM). I contributed at every stage, from researching catchment descriptors that could be coded for the data to utilising machine learning to predict median flow and group catchments into ‘pooling’ groups. I also developed a smoothing script to ensure flow consistently increased downstream. When I began, my knowledge of spatial data and using DTMs to predict flow values was limited. However, by developing my skills in R and Python, we successfully achieved the project’s goals.”

Sam Clayton, Data Scientist

Applications to join this pathway are now open! Click here to apply.