Excelsior University is seeking a Decision Scientist to leverage data science to drive strategic initiatives, improve operational efficiency, and achieve institutional goals. Reporting to the Director of Analytics, the decision scientist will collaborate closely with business partners in Marketing, Admissions, Advising, Student Financial Services, and the Office of the Registrar. The role will be responsible for using machine learning, predictive modeling, experimental design, and other analytical techniques to inform strategic decisions and identify opportunities for efficiency and growth. This role gives you the opportunity to lead innovative projects and support Excelsior’s mission of serving adult learners, with an emphasis on those historically underrepresented in higher education.

This position is based out of the university's home office in Albany, NY with the option of considering remote candidates. Remote employees will have mandatory occasional travel for meetings, conferences, and professional development opportunities. Remote employees are expected to be available during work hours 8:30 am - 5:00 pm Eastern time. The University will supply necessary equipment to perform the essential functions of this job (e.g., Laptop, docking station). Employees are responsible for having a workspace where they can participate in virtual meetings without multiple interruptions and noise.

Duties and Responsibilities: 

• Use predictive and prescriptive analytical techniques to enable leadership to make informed decisions.
• Apply demonstrated knowledge of data modeling, business analysis, and statistical analysis to design and evaluate drivers of growth and efficiency.
• Uses various data analytics tools to clean, manipulate, and transform data for analysis.
• Support projects and work with university leaders to identify assumptions for analytical tools and models with the goal of using these tools to provide data driven solutions.
• Communicate with data through written and oral presentations, using data visualization techniques to present findings in a manner that can be easily understood by nontechnical audiences.
• Promote data stewardship and data quality.

Qualifications: To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• Bachelor's degree or higher from an accredited institution in a business analytics, statistics, economics, data science, or relevant quantitative or empirical research field.
• Three or more years of relevant experience, though education may be used along with experience.
• Demonstrated knowledge and application of data analysis and statistical modeling, preferably in an academic or educational setting.
• Proficiency in programming languages such as Python or R, or willingness to learn combined with knowledge of other code-based programming languages such as MatLab, Stata, etc.
• Understanding of database systems and data manipulation techniques.
• Strong interpersonal and communication skills, with the ability to convey complex analytical concepts to non-technical stakeholders.
• Ability to work collaboratively and effectively with academic and business leaders.
• Strong problem-solving skills and a proactive approach to identifying opportunities for improvement.
• Strong attention to detail, thoroughness, and follow through.
• Ability to work independently and meet deadlines.
• Ability to prioritize and manage multiple tasks.

Preferred Qualifications: 

• Master's or Ph.D. from an accredited institution in a business analytics, statistics, economics, data science, or relevant quantitative or empirical research field strongly preferred. Higher education experience highly preferred.

The hiring salary range for this position is $60,000.00 - $75,000.00. The hiring salary range above represents the University's good faith estimate at the time of posting.