Virginia Tech is a public land-grant university, committed to teaching and learning, research, and outreach to the Commonwealth of Virginia, the nation, and the world. Building on its motto of Ut Prosim (that I may serve), Virginia Tech is dedicated to InclusiveVT—serving in the spirit of community, diversity, and excellence. We seek candidates who adopt and practice the Principles of Community, which are fundamental to our on-going efforts to increase access and inclusion, and to create a community that nurtures learning and growth for all of its members. Virginia Tech actively seeks a broad spectrum of candidates to join our community in preparing leaders for the world.
Virginia Tech is looking for a Data Science Post Doctoral scholar in the Office of Academic Decision Support. The mission of the office is to promote and facilitate a culture of continuous improvement in the university by developing systems and providing solutions and analyses to support strategic and tactical decisions. The office promotes systematic reflection and effective policy, program, and resource planning by working collaboratively with university leadership, data stewards across campus, and other staff at all levels of the colleges, academic support, and student life units to provide solutions that support shared understandings of institutional data, helping them to achieve student and faculty success.
Study, research, and develop advanced analytical models that provide insights required for efficient and effective organizational and institutional decision making.
Develop creative visualization tools and libraries to help effectively communicate the analysis results and reports to the administrative and academic departments within Virginia Tech and higher education regulatory agencies.
PhD in Statistics, Operations Research, Mathematics, Computer Science, Physics or other quantitative discipline
Expertise in data mining, machine learning, and mathematical optimization algorithms/techniques
Strong applied and research skills in data-mining and applied machine learning and experience in developing innovative data solutions.
Experience and expertise in exploratory data analysis. Expertise in developing data visualizations using open source libraries
Experience and proficiency in Python, SAS or R
Experience with relational databases and proficiency in SQL scripting
Creative, Enthusiastic, Self-directed, and quick learner
Effective written and oral communication skills
Employee Category: Research Faculty
Appointment Type: Restricted
Tenure Status: Non-Tenure Track
Percent Employment: Full-time
Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, gender, gender identity, gender expression, national origin, political affiliation, race, religion, sexual orientation, genetic information, or veteran status; or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees, or applicants; or any other basis protected by law.
For inquiries regarding non-discrimination policies, contact the executive director for Equity and Accessibility at 540-231-2010 or Virginia Tech, North End Center, Suite 2300 (0318), 300 Turner St. NW, Blacksburg, VA 24061.
Virginia Tech takes a hands-on, engaging approach to education, preparing scholars to be leaders in their fields and communities. As the commonwealth's most comprehensive university and its leading research institution, Virginia Tech offers 240 undergraduate and graduate degree programs to more than 31,000 students and manages a research portfolio of more than $513 million. The university fulfil...ls its land-grant mission of transforming knowledge to practice through technological leadership and by fueling economic growth and job creation locally, regionally, and across Virginia.
Through a combination of its three missions of learning, discovery, and engagement, Virginia Tech continually strives to accomplish the charge of its motto Ut Prosim (That I May Serve).