Ocean Science Consulting Limited is currently seeking a Statistical Modeller.
Vacancy: Statistical Modeller
Type: Full-time, permanent position
Salary: 34,000 – 40,500 depending on qualifications and experience
Location: Dunbar, East Lothian, Scotland, UK
Application deadline: 31st August 2020
Ocean Science Consulting Limited (OSC) is a privately-owned technology-focused marine-science company involved principally in the global supply of underwater noise and marine mammal monitoring, and risk mitigation services. OSC reinvests >80% of profits into Research & Development (R&D), orientated primarily towards high-level research on the harbour porpoise (Phocoena phocoena) and other marine mammal species, Rigs-to-Reefs using Remotely Operated Vehicle (ROV) footage, underwater noise measurement and modelling, and improving marine mammal and environmental monitoring standards worldwide.
OSC seeks to expand its UK-based team. This is a rare opportunity for permanent employment as a Statistical Modeller. The commercial post doc role involves working primarily in OSC’s R&D wing, although suitable candidates may also be considered for a combination of consultancy/commercial duties. It is anticipated that over the course of the first year, the candidate will bring to completion ca. five manuscripts for submission to peer-reviewed journals. Applicants must therefore be able to source, consolidate, analyse, interpret, and present these data in the form of high-level, peer-reviewed papers, that must be brought to completion on commercial and not academic timescales (i.e. weeks, not months), with minimal supervision from line managers. This is a highly unusual position for academic research in a commercial consultancy. The candidate may also be presented with urgent commercial requests as these arise and must therefore be able to switch from one project to another. Prioritization is of high importance.
Additional details can be found on: http://www.osc.co.uk/careers/vacancies/ or https://www.jobs.ac.uk/job/CAO635/statistical-modeller
- Conduct statistical analysis of pre-existing datasets;
- Write manuscripts for submission to peer-reviewed journals;
- Oversee the peer-review process; and support colleagues with analysis of commercial datasets as and when required.
A successful candidate will have the following:
- A completed PhD in a relevant scientific discipline (statistics/oceanography/marine biology/marine ecology, etc.);
- The position requires a PhD; however, students may apply, but be aware that starting salary will be ?27,000-30,000 (depending on qualifications and experience until the PhD is completed), and the position would be part time until the PhD was completed;
- Strong statistical analysis background ideally in R (e.g. GLM, GAM, PCA, HBM, INLA, time series analysis, distance sampling, abundance estimation, survey design, PCoD, etc.);
- A minimum of two, first author, ecological-modelling related peer-reviewed scientific papers in a top journal (i.e. not a proceedings paper);
- Experience sourcing and processing oceanographic datasets (synoptic satellite-derived or modelled data, etc.) and handling data of various types including: netCDF, csv, txt, etc.;
- Excellent spoken and written English (to peer-reviewed, non-copy-edited level);
- A genuine interest in marine mammals and benthic ecology, and an understanding of the physical parameters of the ocean which affect them;
- Ability to collaborate within a team setting to produce high-calibre publications and reports; and,
- Must be legally allowed to work in the UK prior to employment (we cannot assist with visas).
- Machine learning and image processing (of ROV imagery);
- Experience making publication-quality maps in QGIS; and,
- Experience with referencing software (e.g. EndNote).
Interested candidates should send a CV and cover letter to: email@example.com. This address can also be used for informal inquiries. Successful candidates will be invited to an interview via Microsoft Teams. An interview task will be provided comprising both written and practical components. The successful candidate will be working under the supervision of Dr Victoria Todd and Dr Laura Williamson.