Faculty of Practice, Business Analytics

6 days ago Be among the first 25 applicants

• A Master’s degree (e.g., MS in Data Science, Business Analytics, Computer Science, Statistics, MBA with an analytics specialization, or related field) is required.
• A minimum of 5-7 years of full-time, practical experience in a senior data science, business intelligence, or quantitative analysis role is required. This experience must be sufficient to demonstrate subject matter expertise.
• Expert-level proficiency in at least one statistical/programming language (e.g., Python or R) and expertise in SQL and relational databases.
• Demonstrated proficiency with current business intelligence and visualization tools (e.g., Tableau, Power BI).
• Strong oral and written communication skills are essential for lecturing, facilitating case-based discussions, and providing constructive feedback on student work.

• Prior teaching experience at the university level (adjunct or corporate training) is highly beneficial, especially experience with business analytics-related courses.
• Cross‑disciplinary teaching expertise in a secondary business analytics‑related, decision sciences field (e.g., operations management, supply chain management).
• A terminal degree (e.g., Ph.D., Ed.D.) is valued but not required, as professional credentials are the priority.
• Experience leading and managing a data analytics team or department.
• Certification and extensive experience with major cloud platforms (e.g., AWS, Azure, or Google Cloud) and big data technologies (e.g., Hadoop, Spark).
• Experience with machine learning frameworks and deployment (MLOps).
• Experience in healthcare analytics, health informatics, or a related field.

• Teach a full‑time course load (typically 4 courses per semester) at the undergraduate and graduate levels, covering foundational and advanced business analytics (e.g., Data Visualization, Predictive Analytics, Database Management, Data Mining, Statistical Modeling, and Business Intelligence) and potentially a secondary field based on expertise and experience.
• Continuously update course content and labs to reflect the latest advancements in data analytics technology and methodologies (e.g., Generative AI applications in business).
• Structure courses to be heavily project‑based and hands‑on, ensuring students are proficient in using industry‑standard software and programming languages.
• Emphasize the ability to translate technical analytical results into clear, actionable business recommendations for non‑technical stakeholders (data storytelling).
• Incorporate instruction on data privacy, security, governance, and ethical use of data and AI in business contexts.
• Advise students on course selection, academic plans, and career paths, leveraging professional experience to guide those interested in graduate school and careers in the data field.
• Participate in departmental, college, or university service duties as appropriate.

Mid‑Senior level

Full‑time

Education and Training

Higher Education

Cleveland, OH $96,651.00-$ 2 weeks ago