Project 1: People+Skills=Match
Challenge: How can people find the right job quickly and simply?
Proposed Solution: Multi-dimensional analysis of who your are + the nature of the work → MATCH
Datasets: Job postings
Project Champion(s): Kamal Dhanoa & Adrianne Lee
Project 2: Training Day
Challenge: How to find the right training for your career transformation?
Proposed Solution: Use job postings and curricula to build online tool that provides information about how and where to find training for the most sought out skills.
Datasets: Job postings, Curricula
Project Champion(s): Jasmine Qi
Project 3: Employee challenges
Challenge: How to measure competencies without relying too heavily on credentials?
Proposed Solution: Scale methods for issuing challenges to potential employees so employers no longer rely on credentials (and interviews) to evaluate potential.
Datasets: Job postings
Project Champion(s): Kelly McGahey
Project 4: Reskill
Challenge: Hiring often is based on credential/profession only... not the experience, aptitude, capacities
Proposed Solution: Tool which assesses skills in job postings and generates quick information outside of job title/credential so that individuals can conduct gap analysis and determine path to transfer and reskill
Datasets: Job postings
Project Champion(s): Robin Wisener
Project 5: Mentor Me
Challenge: When a person is struggling with a sense of dread regarding their career prospects, he turns to Google for help and falls on a virtual mentor and decides to strike up a conversation
Proposed Solution: A virtual tutor that remembers you from past interactions and is invested in your career mobility and agility. An app where the machine learning /AI is local to the device.
Project Champion(s): Mundeep Gill
Project 6: Future jobs
Challenge: Youth struggle to access skills training and know what is needed in the workforce
Proposed Solution: Forecasting tool that identifies trends using job postings and required skills.
Datasets: Job postings, Brookfield/McKinsey Automation
Project Champion(s): Carl Neustaedter
Project 7: Auto-Vulnerable
Challenge: What impact will automation have on Ottawa’s most vulnerable?
Proposed Solution: Explore the relationship between sector level automation and Ottawa’s labour force and socio-economic risk at a neighbourhood level to identify who and where populations are most at-risk. Let's develop an automation vulnerability index by: neighbourhood, multi-level, software solution to identify and measure exactly who is at risk and how much to trigger policy levers.
Datasets: Brookfield Automation data, Specially obtained employment/demographics data
Project Champion(s): Paul Steeves
Project 8: Digital Union
Challenge: How do we build social, political and economic capital for the chronically unemployed?
Proposed Solution: A union for the unemployed through a digital platform
Datasets: Sector-based data - where is chronic unemployment, demographics on who it impacts and what priorities of this population are
Project Champion(s): Pat Whelan
Project 9: I want to be
Challenge: How can youth and job seekers map their desired career and develop a plan to develop the required skills?
Proposed Solution: Develop an ‘I want to be...' tool that generates skills, knowledge, training, education required for tomorrow’s jobs
Datasets: Job postings, Jobs classification data
Project Champion(s): Ashraful Hasan, Adam Moscoe
PROJECT 10: TARGET: THE NON-TRADITIONAL CANDIDATE
Challenge: AI screens out the non-standard talent profile – causing unintended issues for otherwise talented people to get hired (and for employers to find great talent). Non-traditional markets include people with disabilities and people returning to the workforce after a period away.
Proposed Solution: Build profile of “non-traditional” candidates, and leverage search and outreach to actively recruit non-traditional hires.
Datasets: Resumes, LinkedIn data
Project Champion(s): Rich Donovan
Project 11: Good decision
Challenge: How do we prevent people and their advisers from making bad investments of time, money, etc. as they pursue employment?
Proposed Solution: Create searchable app that students/educators can use to know how in-demand or likely to be automated their skills are.
Datasets: Brookfield Automation Data
Project Champion(s): Sid Bhargava
Project 12: Newcomers
Challenge: Which newcomers to Canada could see their jobs automated within 5-10 years?
Proposed Solution: Data visualization of immigrants coming into Canada indexed against automatable jobs.
Datasets: Brookfield Automation Data, Newcomer skills profile
Project Champion(s): Fred Ninh
Project 13: do you speak labour market?
Challenge: How to equip people with user-friendly information to inform career decisions?
Proposed Solution: Make labour market info more accessible, user-friendly and understandable.
Datasets: Labour force trends
Project Champion(s): Daniel Komesch & Ingrid Argyle
Project 14: Gig to live
Challenge: Gig workers struggle to know how much they need to work and at what compensation level in order to be financially stable.
Proposed Solution: A budgeting and financial modelling tool that builds a profile of the worker to determine income they need to be financially secure and tracks that against income over time.
Project Champion(s): Nem Zutkovic, Kang Hai Chen
Project 15: workforce shift
Challenge: How can large businesses evaluate and plan for the impact of automation on their workforce and the required supports?
Proposed Solution: Build a tool which includes jobs data and employee data for Acme Inc. Model the workforce transition over time (5, 10, 20 years)
Datasets: Sample dataset of workforce from large financial institution
Project Champion(s): Tony Huang