Employment Innovation Fellowships sponsored by the Ford Foundation Applicants Lance Legel Megan Majd Alumnus (Spring 2014) Resident [email protected][email protected]954-740-0845 310-706-5221 Subject We propose to build a web platform to connect people to the most useful skills. This platform will be based on a search engine, which enables discovery of what skills are most demanded across jobs. Our minimum viable product could be significantly helpful for youth who lack intuition about what skills they should learn for the jobs they may desire pursuing. This could be a first step toward an integrated platform for discovering skills, mastering skills, and connecting skills to employers. Angle Globalization and information technology – e.g. the World Wide Web – have disrupted virtually all professions, and will increasingly do so in coming decades. In a recent review of labor economics 1 , a report to the National Bureau of Economic Research 2 concludes: with the rise of artificial intelligence and robotics that can do many jobs of knowledge and physical workers, imbalance between supply and demand of skills is likely to increase – potentially to a breaking point for the whole economy. In the confusing disruption, young people especially often end up empty-handed, at various stages of the discovery-to-education-to-employment pipeline: 1. Discovering skills most useful 2. Mastering skills in theory and practice 3. Connecting skills to employers Fortunately, new solutions and initiatives are closing leaks in the pipeline. For example, LinkedIn is now seeking to offer a systemic and scalable solution across the second and third parts of the pipeline. Historically focused on connecting skills to employers, it recently acquired Lynda.com for $1.5 billion, to help its 350 million users master skills they need to get jobs they seek. Meanwhile, initiatives by broad coalitions (e.g. RTI, World Bank) are essential to long-term equitability, and so we value these efforts no less. We propose to integrate with all of this by starting with a focus on the first problem in the pipeline: what to learn? Professionals have some intuition about what skills are most useful and most demanded in their chosen line of work. But youth often lack the exposure to the market in the first place to identify what’s best to learn. So young people may especially benefit from a platform that helps them quickly extract intuition about what skills are most needed for jobs they want. Meanwhile, even experienced professionals, perhaps in transition from one job to the next, or perhaps seeking to refine their craft, would be able to use our proposed platform to acquire a statistical intelligence about what they may want to learn next. 1 Computerization, atomization, crowdsourcing and the new economics of employment (Feb. 2015) 2 Robots Are Us: Some Economics of Human Replacement (Feb. 2015)
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Employment Innovation Fellowships sponsored by the Ford Foundation Applicants
Subject We propose to build a web platform to connect people to the most useful skills. This platform will be based on a search engine, which enables discovery of what skills are most demanded across jobs. Our minimum viable product could be significantly helpful for youth who lack intuition about what skills they should learn for the jobs they may desire pursuing. This could be a first step toward an integrated platform for discovering skills, mastering skills, and connecting skills to employers. Angle Globalization and information technology – e.g. the World Wide Web – have disrupted virtually all professions, and will increasingly do so in coming decades. In a recent review of labor economics1, a report to the National Bureau of Economic Research2 concludes: with the rise of artificial intelligence and robotics that can do many jobs of knowledge and physical workers, imbalance between supply and demand of skills is likely to increase – potentially to a breaking point for the whole economy.
In the confusing disruption, young people especially often end up empty-handed, at various stages of the discovery-to-education-to-employment pipeline:
1. Discovering skills most useful 2. Mastering skills in theory and practice 3. Connecting skills to employers
Fortunately, new solutions and initiatives are closing leaks in the pipeline. For example, LinkedIn is now seeking to offer a systemic and scalable solution across the second and third parts of the pipeline. Historically focused on connecting skills to employers, it recently acquired Lynda.com for $1.5 billion, to help its 350 million users master skills they need to get jobs they seek. Meanwhile, initiatives by broad coalitions (e.g. RTI, World Bank) are essential to long-term equitability, and so we value these efforts no less. We propose to integrate with all of this by starting with a focus on the first problem in the pipeline: what to learn?
Professionals have some intuition about what skills are most useful and most demanded in their chosen line of work. But youth often lack the exposure to the market in the first place to identify what’s best to learn. So young people may especially benefit from a platform that helps them quickly extract intuition about what skills are most needed for jobs they want. Meanwhile, even experienced professionals, perhaps in transition from one job to the next, or perhaps seeking to refine their craft, would be able to use our proposed platform to acquire a statistical intelligence about what they may want to learn next.
1 Computerization, atomization, crowdsourcing and the new economics of employment (Feb. 2015) 2 Robots Are Us: Some Economics of Human Replacement (Feb. 2015)
Why do we not already have such a platform? Surely, historically, it has been impossible: all the data was disparate in siloes of newspaper classifieds, and so on, while statistical algorithms for effective data science (e.g. “natural language processing”) were crude and inaccessible. Only recently has all of this become readily and widely available across the World Wide Web. Open source libraries of the appropriate algorithms, and a virtually endless supply of cheap cloud computing resources for processing all of the data as needed, have mostly emerged only in the past five years or less. Structure We aim to develop an online web platform for discovering important skills. To do this we will scan thousands of job postings on the World Wide Web, extract the skills that are identified in these postings, organize this information in databases, and then make this easily accessible through a user interface similar to Google Search.
The online graphical user interface for the minimum viable product that we propose could be very simple. Imagine that upon visiting a webpage www.[x].com the user is shown a minimalist set of options, with no distractions, advertisements, or anything like that. There is only a search bar that a user can type text into and press “enter”, just like at Google.com. There may be a simple question to guide the search, such as “What’s your dream job?” or “What do you want to learn today?”. Notice that these are two different searches – one about jobs, another about skills. Just like Google’s AutoComplete that happens while you type, we will build the job skills search engine to immediately and automatically match what is the most likely job or skill the user has searched for, by referencing two indexed dictionaries of each, which we will have stored in databases. If we identify that the user has searched for a job, we will return as a result a statistical graphical visualization (e.g. see Figure 1) on the skills that are most demanded for the job. If we identify that the user has searched for a skill, we will return in the same fashion a visualization of the top job professions that the skill is in demand for. Both visualizations will be similar, and work on top of the same data, but they just consider the data from different vantage points.
As next steps beyond the minimum viable product – if we are successful, initially – we will aim to help direct users to learning resources (e.g. rated courses like CourseRank.com) and actual job listings that we’ve used to derive our data (e.g. Indeed.com). We do not focus on those steps in this application – despite their awesome potential – because we recognize how challenging and significant it would be to execute just the first task. Prior to developing these components for helping to close the discovery-to-education-to-employment gaps, and only if we meet our metrics for success of the minimum viable product identified in the Takeaway section, we would actively solicit feedback for the total user experience to pursue, through a formal user-centered design process.
There is open source algorithmic precedent for our prototype idea. Recently, a curious data scientist published a tutorial with code for the following3:
• Scanning websites that list jobs • Extracting from the website text the skills identified as needed for those jobs • Computing statistics across all jobs for each skill
See Figure 1 for a visualization of some of the results. Note that in Figure 1, data is derived across the United States, while in that project the developer also collected and visualized data that was local to cities, e.g. New York City. We would be able to do the same, especially if that proved to be easier and smarter for initial launching of the minimum viable product. Ultimately, we would aim to design our data scanning and organizations to provide an easy way for users to filter the geography of their job skill searches.
3 Web Scraping Indeed.com for Key Data Science Job Skills (Mar. 2014)
Figure 1 Visualizing the current demand for skills across data science jobs in the United States (Jesse Steinweg-Woods)
Timeline Here are key milestones of the project, with loosely estimated target deadlines:
• May 15 – All stakeholders have met and agreed on a vision • May 30 – Computer architecture designed for scanning World Wide Web • June 15 – Sources of job listings have been mapped • June 30 – Dictionary of skill names has been defined • July 15 – Parsing of job listings with extraction of skill names: skills in jobs indexed • July 30 – Prototypes of visual analytics on interesting subset of jobs • August 15 – Refining, debugging, organizing databases for production-ready website service • August 30 – Prototype of real-time data retrieval via text query in a search bar at www.[x].com • September 15 – Technical design of user interface architecture • September 30 – Prototype of partially functional user interface with real data • October 15 – Finishing functionality to user interface • October 30 – Fixing general problems and polishing overall quality of user experience • November 15 and beyond – Launch and marketing of minimum viable product
Takeaway Our initial minimum viable product will assist people to understand the job market better, so that they can better educate themselves for it. The core goal is to provide a free search engine through which anyone can identify the skills most demanded for jobs of most interest.
If the primary goal is achieved, then further opportunities open up further down the education-to-employment pipeline. For example, once skills are mapped to jobs being searched, then we would be capable of introducing a useful mapping of the best massive open online courses (MOOCs) to each skill. Then the user can, over long periods of time, enjoy a virtuous cycle of discovering skills, learning skills, and searching for jobs... So beyond showing courses for learning newly discovered important skills, we would hope to be able to eventually pursue providing cohesive support for actually getting jobs for the professions that users search for, as Indeed.com does. This could be possibly integrated with existing leaders, e.g. LinkedIn.
We define multiple key metrics of success, with target benchmarks:
Key Performance Indicator Minimum Goal
Skills Mapped 10,000
Jobs Mapped 100,000
Users by Summer 2016 10,000
Courses Mapped 1 / Skill
Ultimately, we hope our platform will lead as many people as possible to realize their dream jobs. Budget The funds will be primarily, if not completely, dedicated to cloud computing through Amazon Web Services. Because L.L. and his friendly colleagues are expert-level programmers on the tasks at hand, there will be zero cost in terms of actual coding. The costs boil down to matters of scaling up number of computers needed to process terabytes of data across the World Wide Web: it’s likely that we will scan many thousands of web pages, save databases with indexed information on millions of words and phrases, and train models to automatically learn useful representations of this information, all of which will likely take hundreds of computer hours on dozens of high-performance computer architectures working in parallel. L.L. has implemented similar cloud computing projects on about 1/10th of this scale with a budget on the order of $500. The algorithms are mostly the same as what he has worked with in the past, and the costs should scale almost linearly: he estimates being able to achieve the web scanning and database organizing components of the project with under $5000. Remaining funds should be sufficient for maintaining web servers for at least half of a year, during which users around the world could freely search and learn. We allocate up to $2,000 for marketing via targeted methods online such as Google AdWords and Facebook Ads, which enable us to target users typing in things like “find a job”. We would only pay for those users who actually click on our advertisement (typically on the order of $0.50 per new user). Ideally, our platform would be desirable to share organically via social media, especially among leaders tackling this problem, and among those most in need of its solution.
Item Estimation Mapping and processing job skill data across World Wide Web $5,000 Servicing our pre-processed data for our users at www.[x].com $3,000
Marketing and advertising of www.[x].com online $2,000
