KTP Associate - Data Scientist Research Associate

  • University of Kent
  • Canterbury, UK
  • 20/06/2022
Full time Data Science Machine Learning Business Intelligence Software Engineering jobs Data Governance

Job Description

Job details

Job type
Fixed term contract

Full Job Description

NatureMetrics is an innovative, science-based, women-led SME that commercialises environmental-DNA-based biodiversity monitoring solutions at scale. They are world leaders in delivering powerful, scalable biodiversity data collected safely and sustainably using environmental DNA. NatureMetrics works to develop end-to-end and automated tools for biodiversity detection in the field, to be used by non-experts.

We are seeking a PhD graduate who will be responsible for the development and integration of new statistical techniques for addressing two challenges associated with biodiversity surveys using environmental DNA: accounting for false negative and false positive readings, and optimising survey design. These techniques will improve the business’s decision-support tools for nature conservation and restoration, facilitating access to new markets.

Specifically, the aim of this KTP is to embed new knowledge and capabilities of Bayesian hierarchical statistical models developed by academics at the University of Kent and University College London. These models account for false positive and false negative errors in environmental DNA surveys. Through this KTP, these techniques will be integrated into NatureMetrics’ analysis workflows, enabling them to increase confidence levels around the presence/absence or abundance of surveyed biological communities (i.e. collections of species at a site) in downstream products. This will optimise the cost and effort needed to collect and analyse samples.

The Associate will be based at the NatureMetrics’ premises in Surrey and will work closely with the support and guidance of Dr Eleni Matechou, University of Kent, and Professor Jim Griffin, University College London. At the end of the fixed term contract, and upon successful completion of the project, there is a possibility that the role could become a permanent full-time position.

As a Research Associate you will:

  • Upscale existing Bayesian hierarchical models developed by the academic team and their adaptation from single to multi-species within an eDNA context over multiple samples.
  • Embed the knowledge from this KTP into the Data Analysis Team to be propagated through the Products and Commercial Teams into new decision-support tools for NatureMetrics’ clients
  • Work at the cutting edge of biodiversity monitoring using eDNA surveys and develop a complete modelling and software solution by implementing the new model, algorithm and study design tool in accessible and user-friendly software.
  • Work closely with the Business Development and Products Teams to guarantee that the developed models and corresponding software fit business needs and all new market opportunities developing from this project are fully exploited, especially regarding input to materials to market and communicate products, highlighting needs for clients to invest in enhanced products (WP 3.1).

To be successful in this role you will have:

  • A PhD (or equivalent qualification) in statistics, data science, or closely related disciplines that includes substantial development of new statistical methods for ecological data and associated inferential tools
  • Experience in developing new statistical methods for ecological data, and in particular DNA-based survey data, within a Bayesian framework (particularly hierarchical models) and associated MCMC algorithms
  • A recent track record of publication or papers in preparation in leading international journals or conferences of statistical models for ecological data, and especially DNA-based survey data
  • Excellent communication skills to deal with a variety of people at different levels of seniority
  • Leadership attributes and be prepared to take control of all aspects of the KTP project throughout its duration

Please see the links below to view the full job description and to apply for this post. If you require further information regarding the application process please contact the HR Team on CEMSHR@kent.ac.uk quoting ref number: CEMS-149-22