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.

Datasets: TBA

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


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.

Datasets: TBA

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

Big Bold Ideas Session, September 15th, 2017 

Big Bold Ideas Session, September 15th, 2017