LaborX

According to a study by Georgetown University’s Center on Education and the Workforce, the country is expected to face a shortfall of 11 million skilled workers to fill jobs that require a college degree by 2025. Colleges are only graduating 2 million students with four year degrees. 2/3 of jobs will require education post high school but that education doesn't have to come from four year colleges. While most people, including Bill Gates, advocate helping more people acquire four year degrees to meet the challenge, we believe many of those jobs can be filled with trained jobseekers without a degree.

LaborX is a talent marketplace connecting graduates of innovative training programs such as vocational, pre-apprenticeship, bootcamps, and community colleges to good jobs using a skill based algorithm and a portfolio model where jobseekers are recommended based on their skills.

According to the U.S. Census, close to 2/3 of the U.S. workforce does not have a four year college degree. This is exacerbated by that fact that most low income Americans cannot afford 95% of colleges. A LinkedIn study found that 85% of jobs are sourced through networking, which means folks without degrees are doubly disadvantaged because they lack strong networks. The lack of a 4 year degree affects about 100 million Americans in the workforce, 70% of whom have relevant training to fill open jobs immediately. These stats do not account for the effect of COVID on the U.S. workforce (~40 million unemployed) and education system, where many students are reconsidering whether it makes sense to go back to college. 

LaborX has created the first talent marketplace focused on jobseekers trained by four year degree alternative programs. Employers can find graduates of the most innovative programs and filter them based on their training and skills. We are building a machine learning network algorithm based on credibility propagation. The algorithm will allow employers to rank candidates based on likelihood of success in a role based on building a network of folks with similar skill attributes, such as folks who graduated from vocational, community colleges, and bootcamps and excelled--empowering the de-risked hiring of candidates with non-traditional education backgrounds.

LaborX serves a wide range of jobseekers including: degreeless, opportunity youth, immigrants, veterans, racial and ethnic minorities, women, justice involved, differently-abled and other low opportunity groups based on geography and demographics. We source and serve these individuals by partnering with training programs that have specific demographic focus, such as Year Up with opportunity youth, Npower with veterans, The Last Mile with justice involved, Upwardly Global with immigrants and refugees, and Sabio with women and underrepresented minorities. 

To build our algorithm, we are identifying successful candidates that have already graduated from these programs, creating a neural network that allows us to quantify our ability to connect people that we expect to perform similarly in a given role, even if those people lacked the common attributes that recruiters typically rely upon—such as four-year degrees from the same university.

According to a LinkedIn study,  85% of people access jobs via networks. Because networks are a flawed proxy for skills, we are expanding and opening access to traditional networks. Traditional networks don't include subnetworks of people who broke in without traditional attributes.  Our algorithm will empower those with nontraditional education backgrounds to utilize network credibility in the ways traditionally used by those with degrees who come from higher socio-economic classes. We want recruiters, not just those looking for diverse talent, to access and hire people from the subnetworks of alternatively trained talent, in effect democratizing network effects.

Our traditional competitors connect people based on networks defined by  education, affinity, referrals, and experience. These include LinkedIn (network based) and Jopwell (education, affinity and network based). Opportunity at Work, a nonprofit which is in pilot mode and has yet to publicly launch their marketplace, is our most direct competitor. They have a similar demographic focus as us (STARS- Skilled ThroughAlternative Routes, which they measure at 71 million Americans)  but as far as we know, are not using Machine Learning propagated network algorithms to recommend and rank candidates. Since most of the people we serve either do not have a four year degree or lack a degree in their field of work interest, our approach is looking to democratize the network effect that tends to benefit those from higher social economic status and with advanced and elite educational pedigree. 

We use network science based on training, skills, and successful employment outcomes of previously alternatively trained talent to improve outcomes from matching/ranking algorithms that are often used for candidate discovery and hiring practices across the industry. A key advantage of the methodology we adopt is the ability to level the "candidate search" playing field without needing the recruiter to be mindful of it, therefore allowing candidates with non-traditional academic backgrounds to feature in search results if their skill set and experiences can satisfy similar job roles.

We leverage modern network science and machine learning to create a novel way of ranking candidates for a given job opportunity. Utilizing this technique allows for inclusive hiring, thereby providing a way for those bereft of a traditional academic background (eg: four-year bachelor's degree) to be considered for roles that have historically focused on candidates with traditional backgrounds. While the latter have the benefit of vast and extended networks of alumni - school and past jobs - the former often lack these resources which are often key to job safety and being considered for new opportunities. By generating "informal" networks and propagating credentials, our solution paves the way for individuals with non-traditional backgrounds to benefit from network effects enjoyed by individuals with traditional backgrounds, which in turn helps even the playing field.

Several technologies use graphs/networks as an underlying component, especially prevalent in social technologies- LinkedIn, Facebook. One of the main features of the "inclusive" hiring platform is the generation of "informal" networks that span across both candidates with traditional and non traditional backgrounds. Additionally, we allow for feedback collection through endorsements and aim to propagate credentials through the network. Broadly, the area of technology spans multimodal graph based social network analysis. The following provides a brief list indicative of the motivations that have shaped the project in its current form.

Reputation and success in Art (https://science.sciencemag.org/content/362/6416/825)

Visual Analytics with SNA (10.1109/TVCG.2013.223)

Information propagation: Flickr (https://doi-org.ezproxyberklee.flo.org/10.1145/1526709.1526806)

Modeling information propagation (http://proceedings.mlr.press/v28/gomez-rodriguez13.html)

Toolbox (https://snap.stanford.edu/snappy/)

Endorsement deduction and ranking in social networks (https://doi-org.ezproxyberklee.flo.org/10.1016/j.comcom.2015.08.018)

LaborX believes in a future where every low opportunity individual will be able to connect to work based on their abilities and break the cycle of poverty.

ACTIVITIES

• Identify, vet, and aggregate talent from top training programs.

• Identify, vet, and aggregate employers who offer living wage jobs open to expanding their pipeline

• Rank and match using skill and network propagation based algorithm

INPUTS

• Partnerships with top innovative local and national training programs, local government agencies, and foundations

• Partnerships with individual and and portfolios of companies as well as placed based and industry specific business associations, roundtables, and initiatives 

• Staff, funding, and technology to create a talent marketplace, matching and ranking algorithms, and marketing collateral

OUTPUTS

• Strong digital portfolio creation

• Jobs with flexible education requirements

• Unbiased matches based on skills that correct for homogenous networks

OUTCOMES

• Profile reviews

• Interviews

• Living wage job offers

IMPACT

• Job placement in living wage jobs 

• Reduced under-employment and unemployment 

• Reduced poverty

                    2020 YTD             2020           2021               2024

Jobseekers        2,171             4,400         17,333         1,338,620

Employers           312             1,100            4,333           334,660

Trainers               42                 54                181            13,980

Our goals for the next year are to go from two U.S. cities to five and rapidly scale to 21 major U.S. cities shortly after. The numbers above only reflect our U.S. projections. By year 5, we expect to serve millions of jobseekers globally in markets across Asia, Latin America, and the African continent. We have already gotten invitations to partner and launch in India and Mexico. Our goal is to revolutionize hiring such that employers will no longer rely on degrees or referrals to the degree they do today and instead trust alternatively trained talent. Our work is one based on deep, bottoms up partnership that serve the specific needs of communities of jobseekers, trainers, and employers. We also believe our work will help further strengthen the alternative education ecosystem by incentivizing more focused, skilled based training programs that are accessible to lower opportunity populations around the world. This, in turn, will reduce poverty across the world in a sustainable way and will not depend on entire education systems to be reformed to serve and prepare all equally. 

Health: COVID 19 has presented a really big challenge as companies are shrinking their workforce and work to adapt to a new normal. 

Culture: White supremacy, racist, nationalist, and nativist views are cultural barriers for many U.S. work cultures to overcome and implement less biased hiring strategies that diversity their workforce.

Legal: In the U.S. the legality of hiring quotas is also a controversial topic in many workplaces. 

Technical:  On the trainer side, the workforce development industry is very low-tech and slow to adapt technology. On the employer side, the key technical barrier is the onboarding of companies and their current employees to use the hiring platform. This is important to both grow the network and to allow for fine tuning of parameters that impact the performance of the ranking algorithms used internally. Moreover, since the underlying mechanism relies on generating "informal" networks that connect the non-traditional and traditional candidates, it becomes important to have more of both types in the database.

Financial: A majority of our talent partners are workforce programs funded by federal, state, and local governments and operate at limited, place-based focused scale and on limited budgets. Sales and payment cycles are months long and make recurring revenue a challenge. On the employer side, many companies pay per job post when hiring or based on successful hiring outcomes.

Health: COVID 19 is forcing companies to reimagine remote work, opening opportunities up for jobseekers who lack transportation or dont live in close proximity to big employer hubs. It de-emphasizes in person networking and relies on more automated tools that more democratically source talent.

Culture: The U.S. is going through a racial equity reckoning that is forcing major discussions on systemic reforms, from the justice department to education and the workforce. We've already seen a renewed interest by partners to start or continue diversity and inclusion initiatives that were more symbolic and performative in the past. 

Legal: Given the cultural transformations happening, once they take hold at the company level, employers lean in and become more vocal about inclusiveness, tracking goals and outcomes, reporting their diversity with pride, making the quota discussion less relevant.

Technical:  Our platform is automating hiring process by integrating into an employer's existing applicant tracking system, thereby reducing the work they need to do to post on yet another job board. 

Financial: Our coalition-building approach of partnering with existing grantees of local, state, and federal governments has given us the credibility to successfully win local government contracts in San Francisco and with more agile, philanthropic funders who see the value of technology and serve as channel partner, funder, and catalyst for technology adoption. On the employer side, we are piloting a private equity portfolio approach with Blackstone to reach their entire portfolio and sell yearly memberships in bulk to their companies.

NA

Full-time 2

Part-time 2

Other 2

Yscaira Jimenez is the founder & CEO. She’s worked for 3 education startups bringing tutoring to 10,000 low opportunity students. She's a serial entrepreneur with 15 years of sales, operations, and hiring experience across 3 education and 2 social enterprise startups, the last two as founder and CEO. She has a bachelors from Columbia University (03), an MBA from MIT (14), and speaks 4 languages.

Sophia De Castro- Sophia is a digital marketing intern. She is currently a rising sophmore at Claremont McKenna college.

Sergiy Lytvynenko is the engineering lead. He brings over 10 years of experience as a full stack engineer. He previously worked at Pathbrite, a digital portfolio company used by over 5 million students.

Victor Popov is the design lead. He brings over 8+ years of experience in UX/UI and has worked with LaborX from the beginning. 

W. Spencer Gusberg is the data science lead. He has over a decade of experience designing, developing and implementing custom machine learning and optimization algorithms for Fortune 500 companies and large institutions. He is a full stack developer and has an MBA from MIT.

Abhishek is a data scientist with a background in algorithms, statistical modeling, network science and machine learning. He has a masters degree  from MIT, and a bachelors degree from IIT. He has previously published works in areas of data-driven modeling and algorithms in transportation.

Our channel partners, which are or have been customers help us map and launch ecosystems and include: 

The Annenberg Foundation

The Gates Foundation

The City and County of San Francisco Office of Economic and Workforce Development.

Our training partners are where we source our talent from and include:

Always Hired

App Academy

BAVCPart

BaycatPart 

CSU Dominguez Hills

City College of San Francisco

Code Tenderloin 

Codetalk 

Coding Dojo 

Evolve Entertainment  

Flatiron School  

Foundry College

Galvanize SFP

General Assembly

GrowthX Academy

Hackbright Academy

Holberton

JVS SF

Job Train 

JobReady

Make School

Mission Bit

Mission Economic Development Agency 

MissionU

NPower SF

Next Chapter 03/2020 

Npower NYC

Oakland PIC

Open Tech Initiative 

Perscholas NYC

Rithm School

Goodwill SF

SF Tech Council

SFSU CS

STEM Advantage

Sabio Featured

Samaschool SF

Success Center SF

TechHire LA 

TechSF Affiliates

Techtonica

The Last Mile

Trilogy

Udacity 

Unlock Academy

Upwardly Global Bay Area

Virtanza

Year Up Bay Area Featured

Year Up Boston

Year Up Chicago

Year Up Los Angeles 

YearUp New York

dev/MissionPart

To date, weve had close to 300 employers, including Google, Airbnb, Salesforce, Slack, Lyft, and Twilio.

Jobseekers: Cost-Free

We provide low opportunity jobseekers with access to career launching, living wage jobs and tools and resources to strengthen their job profiles via our LaborX talent marketplace.

Employers: Cost-SaaS Membership $3k, $6k, $12k per year

We provide employers with access to trained, untapped, diverse talent from local and national innovative training partners via our LaborX talent marketplace. Membership tiers are based on number of jobs posted, teammates on the account, and access to features such as algorithmic ranking.

Training Partners: Cost-SaaS Membership $1.2k, $3k, $6k per year

We provide training partners an applicant tracking system to track their jobseeker placement outcomes and other relevant analytics via our LaborX talent marketplace. Membership tiers are based on teammates on the account and access to features such as analytics and ability to directly recommend jobseekers to jobs.

Ecosystem Partners: Cost-Ecosystem partners sponsor ecosystems of training and employer partners and depends on the number of partner sponsored (# of trainer and employer licenses they want to sponsor). To date, these contracts have ranged from $75k-$400k.

We provide ecosystem partners an applicant tracking system to track their jobseeker placement outcomes at the macro level, broken down by training program and other relevant analytics via our LaborX talent marketplace. Our marketplace powers any relevant job initiatives (Apprenticeship, PledgeLA, etc) for our ecosystem partners.

LaborX has and continues to attract philanthropic, investment, and revenue dollars to sustain our work. 

We have raised a pre-seed round, are actively raising a seed round, and plan to raise an A round in the next two years.

We are revenue positive and have been profitable since last year. 

We expect to continue raising blended capital and revenue to continue fueling our growth.

We are applying to Solve to work alongside world-class global social entrepreneurs that we can learn from and be thought and operational partners. We already have a strong network of peers that we can also share with our peer Solvers to further amplify their work.  

We are also applying to leverage Solve's global platform for marketing and branding, and partnership opportunities from the Solve community of partners, funders, and supporters. 

We plan to contribute to the community by pushing our peers to think intersectionally about their work and offering support so that their organizations are hiring inclusively and building pathways out of poverty for low opportunity groups around the world. 

We are looking for employer distribution channels to accelerate the rate of adoption of our platform. 

We are also in need of marketing, media, and branding that will elevate our presence in the spaces where our work is most relevant. These include industry associations and future of work funders. 

We would like to partner with any MIT labs and affiliate corporations who are interested in democratizing the future of work. In particular, the former MIT Inclusive Innovation Challenge/current MIT Solve Economic Prosperity team and their network of affiliate funders, judges, and partners would make great catalytic partners as we look to scale. We are looking for introductions to coalitions of employers, whether placed based or industry focused, that want to create entry points for low opportunity workers via apprenticeship programs and entry and middle skill level jobs, especially internationally.

Upwardly Gloabal is one of our training partners where we source talent that we promote on LaborX for. Upwardly Global is a 501(c)(3) non-profit organization, based in San Francisco with additional offices in New York, Chicago, and the DC area that helps immigrant, refugee and asylee professionals rebuild their careers in the United States.

We partner with Upwardly Global in San Francisco. This prize will allow us to scale that partnership across New York, Chicago, and the DC area, as well as other U.S. cities with high concentration of refugees. 

CodeTalk is an Los Angeles based, digital web technology job training program for low income, underemployed and underserved women. In an intensive and rigorous 16 week program, they provide the skills, tools, training, professional development and support so that their graduates can pursue entry level positions in the technology sector.

They aim to transform the income potential of low income and underserved women by promoting financial stability through training for employment in a technology field. Codetalk's mission is to change the trajectory of their graduates.

Their program accomplishes it's mission through a growth mindset, innovative teaching techniques, a cutting edge curriculum and the idea that we are not defined by or limited to our circumstances. The Codetalk program changes lives.

This program is part of the St. Joseph's Center, which helps 6,500 people every year find hope through empowerment with programs including a food pantry, free all-day childcare, homeless services and housing programs, education & vocational programs, intensive one-on-one counseling and employment development.

We would use the innovation prize to sponsor CodeTalk and grow the number of women we serve through their and similar programs in the city we operate.

In light of the massive unemployment cause by the COVID-19 pandemic, LaborX is launching a feature that will allow our trainings with digital training offerings the ability to recruit and train new jobseekers via our platform. The GM Prize on Good Jobs and Inclusive Entrepreneurship will allow us to rapidly scale this new offering to meet the demands of the growing numbers of unemployed people. This prize will allow us to reach more unemployed jobseekers, particularly those in opportunity zones and from demographics that have been hardest hit by the pandemic.

In light of the massive unemployment cause by the COVID-19 pandemic, LaborX is launching a feature that will allow our trainings with digital training offerings the ability to recruit and train new jobseekers via our platform. The Gulbenkian Award for Adult Literacy will allow us to target this offering to programs that are focusing on advancing adult literacy and rapidly scale this new offering to meet the demands of the growing numbers of unemployed people, not just in the U.S. but globally. As a Portuguese speaker, the CEO can start a pilot in Portugal with little friction. 

We envision an inclusive hiring platform that leverages modern network science and machine learning to create a novel way of ranking candidates for a given job opportunity. In the context of professional hiring, networks can be described in terms of shared academic background (same school, university), or in terms of shared job experiences. While candidates with more traditional backgrounds benefit from large and cohesive networks, those without such backgrounds often identify themselves as part of sparse/small networks and therefore are not able to benefit from network effects like their traditional counterparts. By taking into account several factors at once - academic, professional and career progression given opportunities - the algorithms used in this project lays the foundation for candidates with non-traditional academic backgrounds to benefit from similar network effects as their traditional counterparts. The end result is that candidate search led by recruiters is more equitable. Moreover, we are developing a semi-supervised tracking mechanism for candidate outcomes to allow for rapid feedback collection on each non-traditional hire, so that others with similar background can benefit sooner from the updates in the underlying network and therefore ranking results.

The AI for Humanity Prize will allow us the resources to grow our pilot and scale this technology widely across different locations and low opportunity demographics.

We believe our solution can be scaled internationally to impact millions of lives as we work to connect low opportunity populations to living wage jobs that catalyze them out of poverty. Because our technology is driven by machine learning looking to democratize access to networks, we are uniquely set up to work with populations that have been traditionally harder to serve because they lack access to these networks. The digital nature of how we connect workers to opportunities also allows to create job connections for workers who may be displaced or home insecure and need to work remotely.