Job Description :
Instructor of Applied Environmental Statistics
The Department of Sustainable Resources Management at the College of Environmental Science and Forestry (ESF), State University of New York, invites applications for a tenure-track position as Instructor of Applied Environmental Statistics. This is a teaching intensive position with five to seven courses expected to be taught each academic year (three to four courses per semester). The successful candidate will be expected to : (1) teach courses in applied statistics ranging from large enrollment introductory statistics for undergraduates to more specialized topics for graduate students, including (possibly) biometrics, experimental design and analysis of variance, multivariate methods, nonparametric and categorical data analysis, regression analysis, and sampling; and (2) provide statistical consulting to undergraduate and graduate students and faculty on their research activities.
Starting Salary : $60,000 - $65,000, DOQ
Requirements :
Required Qualifications :
- The selected candidate must hold at least a Master's degree in Statistics or a related applied quantitative field at the time of appointment.
- Demonstrated teaching experience for both undergraduate and graduate students.
- Commitment to diversity and inclusiveness in teaching and service activities.
Preferred Qualifications :
PhD in Statistics or a related fieldExperience teaching courses in a variety of topics including, but not limited to, introductory statistics, biometrics, sampling methods, regression, nonparametrics, experimental design, and multivariate analysis.Experience teaching courses in varied modalities (in person and on-line) and / or with large enrollment.Experience applying statistical methods in one or more areas of environmental science, natural resources, or ecology (., forestry, fisheries, wildlife biology, climate science, or related fields) research.Experience providing statistical consulting for students.Capacity to teach courses in a variety of topics including but not limited to sampling methods, regression, non-parametrics, experimental design, and multivariate analysis.Experience applying data science techniques (., machine learning, statistical modeling, big data analytics) to address complex problems in environmental science and natural resources (., biodiversity, conservation, climate change, forestry, fisheries)