The selected applicant will be expected to work in both an applied and theoretical statistics capacity, developing new and/or extending existing statistical methods as required to solve applied problems in fisheries and conducting data analyses, as part of the IATTC Stock Assessment Program. However, the selected applicant will also be expected to assist IATTC scientific staff members from other programs.
Work in the Stock Assessment Program covers range of topics, which often involve statistical methods development, including population trend estimation, population assessments, spatio-temporal studies of fishing vessel behavior, development of sophisticated data screening algorithms for fisheries data review, and sampling designs for data collection by human observers and electronic monitoring. Several upcoming projects in this Program include: Spatio-temporal modelling of tuna tagging data; Close-kin mark-recapture; Developing a sampling program in Latin America
A PhD in statistics with both theoretical and applied components, or a PhD from a quantitative interdisciplinary graduate program with solid theoretical statistics course work, is strongly preferred, but extensive relevant work experience will be considered for applicants with a master’s degree in statistics or from a quantitative interdisciplinary graduate program.
Candidates should also possess the following skills:
• Proficiency in exploratory data analysis methods, including techniques for multivariate data.
• Proficiency with standard statistical modeling techniques such as generalized linear and additive models, mixture models, for Gaussian and non-Gaussian data, including count data with zero-inflation.
• A solid understanding of theoretical statistics and how to apply this knowledge to create new methods and modify existing methods to solve practical problems.
• Theoretical knowledge and practical experience with a range of spatio-temporal modelling approaches for diverse data types, including approaches for non-stationary data.
• Proficiency in the use of machine learning algorithms (e.g. random forests, support vector machines, clustering algorithms).
Ability to develop and implement simulations and related analyses for development of sampling designs.
• Proficiency with the R programming language.
• Willingness to work in an office setting, primarily with computer databases, computer programs, and statistical software.
• Willingness to travel when necessary.
• Strong inter-personal skills and experience working as a part of a team, as well as working independently.
• Willingness to learn new skills and to self-teach new statistical methods.
• Creativity to adapt current methods or develop new methods to solve practical fisheries problems.
• Excellent communication skills, both oral and written.
• Working knowledge of English or Spanish, and at least reading fluency in English and an ability to hold a conversation in that language.
• Multiple first-author publications in peer-reviewed, quantitative journals.