APTPUO-Winter 2027-MEM5300 B (online)

University of Ottawa • Ottawa, ON

3d • Remote

About The Position

This course focuses on the application of data mining techniques and predictive analytics to business problem-solving. It covers key algorithms and techniques for extracting meaningful insights from business data, including data preprocessing, decision trees, neural networks, k-nearest neighbors, clustering, and association rules. Students will gain hands-on experience with data mining tools and software, applying these techniques in managerial contexts such as customer relationship management, marketing, sales, credit scoring, and churn analysis.

Requirements

• Bachelor's degree in Business, Computer Science, Engineering, or related field.
• Demonstrated track record in professional or managerial roles involving data analytics, data mining, or technology-driven decision-making.
• Proficient in data mining and predictive analytics.
• Ability to teach both supervised and unsupervised learning techniques, including decision trees, neural networks, k-nearest neighbors, clustering, and association rules.
• Extensive experience with IBM SPSS Modeler, including stream creation, model building and evaluation, and applying CRISP-DM within the visual interface.
• Ability to apply analytical techniques to managerial contexts such as CRM, marketing, sales, credit scoring, and churn analysis.
• Solid understanding of data preprocessing, including data cleaning, transformation, and partitioning.

Nice To Haves

• Master’s in Management or Engineering preferred.
• A Ph.D. is considered an asset.
• Experience as a CTO or equivalent leadership role in a data-intensive or tech-focused organization is highly desirable.
• Prior experience in post-secondary teaching or professional development instruction is preferred.
• Familiarity with tools such as RapidMiner, WEKA, and other data mining platforms.
• Knowledge of scripting or programming languages (e.g., Python, R, SQL).
• Experience with integrating SPSS Modeler with business systems or databases.
• Knowledge of modern data analytics trends and use of visual programming tools in business intelligence.

Responsibilities

• Teach data mining techniques and predictive analytics.
• Apply data mining techniques in managerial contexts such as customer relationship management, marketing, sales, credit scoring, and churn analysis.
• Provide hands-on experience with data mining tools and software.

Benefits

• Competitive salary
• Defined benefit pension plan
• Group insurance coverage
• Employee and family assistance program

What This Job Offers

• Job Type: Part-time
• Career Level: Mid Level