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Environmental Intelligence Data Scientist Science

Reference   (Please mention Stopdodo/Environment Jobs in your application)
Sectors Water, Hydrology & Marine Ecology
Salary (Minimum)
Salary (Maximum)
Type Fixed Term and Permanent Roles
Status Full Time
Level Mid Level
Deadline 01/11/2020
Company Name Plymouth Marine Laboratory
Contact Name
Website Further Details / Applications
Plymouth Marine Laboratory logo
Also Listing:

£32200, Scienctist Grade

Full-time; open-ended appointment

PML is looking for an enthusiastic and forward-looking data scientist to develop environmental intelligence applications using Earth Observation (EO) data from satellites, aircraft and drones. The successful applicant will work in the NERC EO Data Acquisition Service (NEODAAS) that provides researchers in the UK with EO data and services and has recently installed a new £1M 40-GPU cluster (MAGEO) with 2PB of storage. The postholder will work with UK scientists, applying machine learning and data science techniques to large EO datasets and will make use of the MAGEO system. They will tackle problems covering timeseries, image analysis and integration of data collected from multiple sources across diverse environmental science areas. Solutions to these problems will likely require technologies across the spectrum of machine learning, ranging from XGBoost and decision trees through to multilayer perceptron's, large scale convolutional neural networks and recurrent neural networks. The work will be driven by end-user requirements and will depend on both well established and state-of-the-art approaches to deliver results and publications.

The position is part of the 40+ person remote sensing team at PML comprising marine and freshwater research scientists, Python Gurus, Data Ninjas, Linux Magicians and web visualisation experts. The group have extensive in-house computing resources available, holding an archive of satellite data which is being added to daily.

Key deliverables of the role:

  • Development of new research and applications utilising a broad spectrum of machine learning techniques in collaboration with NEODAAS end-users.
  • Generation of training material to improve data science skills in both external NEODAAS users and within PML and to increase uptake of Deep Learning techniques.

Required skills and experience:

  • An enthusiasm for working with others to solve problems using machine learning. 
  • A passion for producing well designed and documented code.
  • Computer vision/image segmentation and object detection experience across multiple problem areas.
  • Strong development skills in Python, using key Deep Learning libraries such as Tensorflow/PyTorch.

The following skills and experience would also be beneficial:

  • Experience using Earth Observation data.
  • Familiarity with distributed learning approaches.
  • Container technologies such as Docker and Singularity.
  • Experience using Linux, in particular setting up and managing systems.
  • Experience with MPI and HPC technologies such as SLURM

This post is open-ended and available up to full-time. Whilst candidates will be expected to spend some time in PML’s offices in Plymouth, opportunities for flexible working arrangements may be considered.

PML is a world leader in marine science and have contributed to a better understanding of our oceans and the challenges posed by climate change and plastic pollution by embracing a range of technologies.

PML is committed to equality, diversity and inclusion, and our policy can be found https://www.pml.ac.uk/getattachment/Working_with_us/SN_40_20_PML_Equalilty_diversity_and_inclusion_policy.pdf. We are proud to have achieved the Athena SWAN award as recognition of our achievements in gender equality. As part of this, whilst the selection process will be based on merit, we particularly welcome applications from female candidates, currently underrepresented.

This post is open-ended and available up to full-time. Whilst candidates will be expected to spend some time in PML’s offices in Plymouth, opportunities for flexible working arrangements may be considered.

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