Development of an algorithm to form and reform teams within organization in the most effective way considering the highest likelihood of collaboration - ON-679

Project type: Research
Desired discipline(s): Computer science, Mathematical Sciences, Mathematics, Operations research, Statistics / Actuarial sciences
Company: Pollinate Networks Inc
Project Length: 6 months to 1 year
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Guelph, ON, Canada; Toronto, ON, Canada; Canada
No. of positions: 1
Desired education level: Postdoctoral fellow
Open to applicants registered at an institution outside of Canada: No

About the company: 

Pollinate was started in 2008 as a platform for mentoring and collaboration. The organization’s passion for mentoring and its desire to make a bigger impact resulted in the launching of Pollinate Networks. Pollinate is a human resources technology business that delivers mentoring programs that help drive your team’s productivity.

Describe the project.: 

Pollinate has leveraged current research on mentoring programs and more specifically has amassed both a body of information and a data set on the factors that make matches successful for mentoring. Our research and practice currently focus on pairs and triads. We have an automated system called crosspollinate.ai for matching mentoring pairs and triads based on optimizing groups so each pair has the highest likelihood of collaboration success. Our system has two parts: one that collects strategic information - length and type of experience, learning goals, subject matter expertise, preferences for location, and gender, and one that is a psychometric index designed to reflect collaboration styles and how well they mesh.

Cross-Pollinate.ai's strength in bringing the right people together to exchange knowledge exchange and do important work has inspired clients to ask us to work with them to provide tools that enable them to form and reform teams. As we have created these cohorts and teams using our tools, we've realized that the science behind matching successful groups diverges from matching successful pairs. Our social instincts and intelligence shift with the addition of more people. The factors we consider for matching listed above are accurate, although fewer of them are used to match groups. The forward motion toward goals for a group requires the balancing of several elements in a new way.

Our final product is a Software Algorithm to form and reform teams within and potentially across organizations.

Required expertise/skills: 

Expertise in data analytics, mathematics, and statistics. We need someone to perform literate review research to see if there are existing research data on our topic of interest. Perform research if there are gaps in our data, and use our current data to understand our objectives.