In addition to evaluating each candidate's relative ability, as demonstrated by quality and breadth of experience, the following factors will provide the basis for competitively evaluating each candidate:
Knowledge of effects of biotic and abiotic interactions on the environment; all aspects of data science and data generation, management, quality control, storage, synthesis, analysis, visualization, archiving, and sharing; familiarity with coding in R, Python, etc. on various software platforms; understanding of advanced data analysis methods such as machine learning, mixed effects regression, non-linear regression, time series analyses, lifecycle modeling, etc.
Ability to synthesize diverse, multi-disciplinary, and disparate datasets; and provide constructive comments.
Special Experience: Graduate or post graduate fisheries-related work in statistics; numerical, theoretical, and conceptual modeling; experience with lifecycle models, mixed effects modeling, machine learning; and/or Bayesian modeling.
Special Personal Characteristics: Detail oriented with a demonstrated ability to act independently and with open-mindedness, flexibility, and tact. Specifically, a high degree of personal initiative, dependability, professionalism, and integrity is expected. The incumbent is open to feedback on performance, can adapt to changing challenges, and demonstrates empathy and understanding of stakeholders’ interests.
Interpersonal Skills: Able to work independently and in a team setting; communicate politely, tactfully, and firmly as necessary with members of the public; demonstrate excellent listening skills and effective negotiation skills; and work effectively in a diverse work environment.
