Machine Learning for a Collaborative Innovation and Augmented Decision Making Platform - BC-384
Preferred Disciplines: Computer Science, electrical & computer engineering, statistics, operations research; Master’s, PhD or Post-Doc
Project length: TBD – will work with intern to determine length of time
Desired start date: As soon as possible
Location: Vancouver or Calgary
No. of Positions: 1
Language : English
Company: Output Services Inc.
At Output,, we help the world’s top companies discover and implement disruptive ideas. For the past 15 years we have helped everyone from small startups to Fortune 50 companies develop successful products and services.
Output is a platform and process that helps organizations identify and implement ideas from the people who know the business best: employees, partners and customers. Using the Output Innovation Platform, the most promising ideas are harvested using a scalable and repeatable process that emphasizes implementation. Process: identify opportunities; prioritize ideas; develop concepts; test hypotheses; launch and implement innovations; and make better decisions. This process is cumulative, constantly evolving and improving as the innovation program matures.
The following are research opportunities for research collaboration and internships to advance the Output platform:
Baseball card machine learning:
- Enabling believability weighted decision making by identifying which areas an individual has capabilities and using machine learning to analyze data and predict or correlate other areas of aptitude. As an example for a hedge fund customer, it is interesting to know that 60% of the individuals in the organization believe the Fed will raise interest rates next week but far more valuable and interesting to know that only 30% of individuals who are typically right about interest rate hikes believe the Fed will raise interest rates next week.
- Related to 1 above, identifying high-potential people in your company / network and their areas of aptitude based on their participation and activity on the platform regardless of job title or tasks.
- In the rapid development of an idea from conceptualization into a minimum viable product and beyond using a crowdsourced approach, it is important to allocate “influence points” to individuals that contribute along the journey for the purposes of acknowledgement, reward or otherwise. Using machine learning to help determine the “weight” of an individual’s contribution to a project is important.
Identifying teams or groups:
- Using machine learning to find patterns and identify groups of individuals that highly perform / effectively solve problems working together. This could be particularly helpful to a large organization with multiple office locations
- Using machine learning to analyze characteristics of individuals participating on the platform for the purposes of matching / directing content specific challenges or other innovation initiatives to that individual
- Where individuals are participating on a challenge by submitting ideas, scoring submissions against criteria to "pre-sort" ideas based on relevancy to the topic
Each organization we work with approaches problem solving and developing new ideas in their own unique way. Our process is rigid enough to move customers along the journey yet flexible enough to accommodate each organization’s different ways of working. Using machine learning to analyze data around the challenge process to identify key elements of a successful challenge or project would be very useful.
- See above. This is an invitation for conversation around the research opportunities identified. Specific research objectives and methodology to be agreed upon with researchers.
- Developed with the intern
Expertise and Skills Needed:
- AI/Machine learning, statistical analysis, prediction, decision-making
- Bonus: sabermetrics
For more info or to apply to this applied research position, please