Focus/Career™

Focus/Career™ is unlike any other job search application on the market today. That’s because Focus/Career bases all its matching, exploration, and referral activities on a unique technology that reads, analyzes, and catalogs information directly from resumes and job postings with unparalleled levels of understanding and accuracy.

Unlike other engines which rely on fixed rules and predefined templates, Burning Glass has developed patented technology for resume and job data extraction leveraging a field of artificial intelligence known as Statistical Natural Language Processing (SNLP). Using SNLP technology, our system has parsed tens of millions of resumes and jobs, not only extracting data but more importantly, learning from the way each one was written and structured.

This sophisticated, dynamic process, performed by the system across millions of job transitions, enables Focus/Career to derive and infer as well as extract data, generating the most accurate and richly detailed database in the industry. Overall, our technology captures, analyzes, and codes over 70 data elements for each job seeker and each job. In addition, for each reported skill, our system understands how long, how recently, and in what context (e.g. in a job, a course, etc.) that skill has been used.

The sum total of our advanced technology is a tremendous amount of personalization, significantly more than is offered by other systems. In essence, Focus/Career “gets to know” each job seeker and each job the same way an experienced counselor or recruiter might and on that basis provides the job seeker with job matches, career mapping and exploration tools tailored to his specific skills and other attributes. Just as importantly, our unique approach ensures that Focus/Career offers job seekers the greatest range of opportunities, by bringing together a variety of innovative search methods to find:

• Jobs that are similar to recent work experiences;
• Jobs that leverage underlying transferable skills; and
• Jobs that reflect placements and transitions made by similarly-situated individuals in the past.

The functionalities shown below are available as standalone applications or as component modules of the Focus/Career suite of job search and career development solutions:

 

• One-Step Registration – Just upload an existing resume and Focus/Career pre-fills registration forms while also providing an “Extreme Resume Makeover”, suggesting skills and work activities that may have been overlooked and options for reformatting using a variety of compelling presentation templates.
• ResumeBuilder – Builds a professionally-formatted resume without requiring the job seeker to write, dynamically creating a personalized interview about each prior work experience with questions designed to highlight transferable skills and the kinds of experiences real employers are asking for.
• Intelligent Auto Matching – Delivers highly relevant matches automatically, without requiring users to specify keywords or O*NET codes, based on a deep analysis of each job seeker’s experiences, qualifications, and career path.
• User Active Searching – In addition to automatic matches, Focus/Career gives job seekers full control over their job search with a full complement of traditional and advanced structured search mechanisms. Focus/Career also enables a much more precise form of “in context” keyword search, enabling users to look for a desired term within a specific section of a job posting.
• GetFeedback™ – Helps job seekers understand the qualifications and experience they need for successful placement into the jobs that interest them.
• SuggestTraining™ – Identifies a job seeker’s skill and credential gaps and identifies local academic and skills training courses that can help resolve those gaps.
• Career Pathways. Suggests possible next steps based on successful transitions made by similar people and maps out how a job seeker can get where she wants to go from where she is today.
• Job Leads That Follow You – Sends strong matches to job seekers as soon as they are listed, giving instantaneous access to highly relevant leads as soon as they become available.

Beyond helping job seekers, students, and their counselors identify promising opportunities and explore personalized career and training options, Focus/Career also supports the business community in its talent acquisition efforts.

As job seekers enter their resumes, Focus/Career builds a detailed database of the skills they can provide to meet the needs of local/regional employers. The database supports a variety of self-service employer-operated functionalities, including:

• Automated job seeker referrals based on a saved set of employer requirements, leveraging the same predictive matching engine in use by virtually all of the world’s top recruitment agencies
• Intelligent searching, which enables employers to search directly from free text job descriptions
• Quick job posting, as simple as submitting a free-text job description
• A job posting writer, with a simple, easy-to-use interface that analyzes basic job information and asks the employer a battery of structured response (yes/no or multiple choice) questions to elicit duties, qualifications, and requirements in order to create more effective job postings

Through the deployment of its GetFeedback™ and SuggestTraining™ modules, Focus/Career also provides robust support to incumbent worker programs, assisting employers to improve retention rates for employees through support of their continuing professional development, enabling them to evaluate how their current skills match up against other individuals with the same job title while identifying possible avenues for further training at local institutions.

Burning Glass	Others
Focus/Career works automatically without requiring users to specify keywords, expanding access by providing highly relevant matches regardless of a job seeker’s (or employer’s) computer or job search expertise.	Other tools require job seekers and employers to structure search queries or specify keywords. This poses problems for the many job seekers who lack the computer and/or job search literacy to know which terms to specify.
Focus/Career supports automated job referrals on a fully user-passive basis, which means that users need only upload their resumes to begin the job matching process.	Other tools require that users participate in active searching, which can be time consuming as well as demotivating.
Focus/Career leverages its understanding of past patterns of placement to deliver the broadest array of relevant options for careers in transition.	Tools that rely on keyword, concept-based, and other semantic engines find jobs on the basis of word overlap and correspondence. Relevance is limited because the systems are not sensitive to context and they are also easy to manipulate. Finally, keyword search technologies often require a “direct hit” -- if the search term is “accountant” but the job title is “CPA”, a keyword engine won’t recognize their relevance. Engines that use O*NET code-based matching are also prone to faulty or irrelevant results, both too broad and too narrow, and are highly vulnerable to miscoding.
Focus/Career matches holistically based upon a full evaluation of each job seeker’s career history, considering each work experience, skill, and qualification, as well as the sequence and context in which they were accrued, and then comparing them with placement patterns for others with similar career attributes. The results are matches that make intuitive sense, regardless of whether they happen to fall within the right code or category. Focus/Career includes a proprietary, state-of-the-art auto-coder for a range of match dimensions, including O*NET, but codes are just a few of the dozens of variables it takes into account when rendering a match.	Job match engines that are based on correspondence of O*NET or other codes face multiple challenges: O*NET classifications can be quite broad, encompassing a wide array of often disparate job titles and work activities and without regard for industry or seniority. As a result, matches driven by O*NET often seem irrelevant. Code-based matching is designed to filter out opportunities from different codes whereas, in reality, there may well be opportunities filed under different codes that leverage similar skills or experiences. Code-based matching is only as accurate as the coding provided by job seekers and employers ... which can be quite inaccurate. O*NET matching systems do not provide sufficient basis for identifying opportunities for people from industries in transition.
Focus/Career recognizes that a person is more than just a list of skills -- the context in which those skills or experiences were accrued makes all the difference. This awareness removes the semantic ambiguity that goes along with using keywords and means the system can infer skills that a job seeker is likely to have but forgot to mention. This is particularly important for job seekers who have difficulty enunciating transferable skills or specifying the terms that employers may be using in their searches.	Keyword and other semantic technologies depend upon users to know exactly which terms to specify; if a user doesn’t list all of the possible synonyms, she may not be matched against otherwise appropriate jobs. This presents challenges for those without significant job search experience or good computer skills. In addition, keyword systems essentially rely on gross arithmetic, ranking documents according to the number of times a specified term appeared in each one. Word frequency does not always reflect relevance, but these engines must rely on crude measures because they lack any awareness of the context in which the specified term appears.
Focus/Career is unique in that it automatically updates its underlying tables and dictionaries and adjusts its predictions to reflect emerging trends – no client maintenance is required.	Skills-based and so-called “expert” systems depend upon complex matrices, skills trees, and rules sets that must be populated and maintained by users and/or system administrators. Other technologies demand that administrators populate vast transition matrices. In either case, clients must expend both time and cost.