AIBusiness recently met with Elise Runde Voss, CEO & Co-Founder and Dan Elbaz, CTO & Co-Founder of Upscored, an AI start-up based in New York City looking to revolutionize the field of Recruitment. Here’s what they have in store for candidates and business looking to engage in intelligent employment future.
Tell us about your journey so far with UpScored – what was the inspiration for the business, and what are
you aiming to achieve?
UpScored cuts through the clutter of recruiting by intelligently connecting candidates to their best job prospects, and companies to top talent. Our Company’s data science platform exceeds the capabilities of job boards and talent
marketplaces with the UpScore. The UpScore is a proprietary algorithm – it formulates a graded score that reflects
the relevance of a job seeker’s resume to a company’s job description. UpScored acts as an advanced router,
analyzing thousands of data points on the candidate’s resume while incorporating user preferences and proprietary
taxonomies. In turn, job seekers can focus on the right opportunities and hiring teams can target talent with the
The idea for UpScored was born from our Founding Team’s personal experience. Prior to UpScored, Dan, Robert,
and I worked together while building Point72 Asset Management’s (formerly SAC Capital’s) Big Data Strategy
Group. During our time at Point72, we experienced firsthand the challenging process of hiring a team – and how
candidate choices can significantly impact a group’s growth trajectory, results, and culture.
We’re passionate about using data science to eliminate the noise in the labor market. UpScored is the first and only online talent marketplace that connects candidates to their best-suited job prospects by using data science and automatically learns from candidate career interests. Our vision is to be the global solution for the modern talent market – where data is combined with human intelligence to create the best possible job matching experience.
Can you give a brief overview of your proposition, and the benefits it offers to an Enterprise?
UpScored fundamentally changes the way companies hire by equipping talent acquisition teams with actionable data
and tools to identify and prioritize top candidates. The human resources industry has grown to $400B in the US
alone, yet HR professionals still spend hours manually sorting through applicants – oftentimes missing over 50% of
the candidate pool. UpScored expedites the search timeline by leveraging smart data sourcing and natural language
processing to rank candidates based on specific experience and skills. Our technology significantly enhances the
hiring process by improving efficiency and candidate selectivity. For job seekers, UpScored matches candidates
with opportunities suited specifically to them while adapting to their career interests.
Current recruiting tools and talent marketplaces use simple keyword matching and basic filtering which lead to poor
matching and inefficiencies in the market – particularly for ambiguous titles such as project manager or data
scientist. Thus, job seekers and companies cannot find each other. UpScored’s key differentiators include: 1)
Automated Scoring: resumes are scored against thousands of job descriptions in seconds, which saves job seekers
and hiring teams time and headache; 2) Adaptive Intelligence: the technology learns from user preferences in realtime via user feedback; and 3) Deeper Analytics: natural language processing and machine learning is applied to the most convoluted part of the recruiting funnel to create a seamless experience.
Would you like to share a couple of your case-studies/success stories? What’s next for UpScored?
Talent acquisition teams spend too much time combing through resumes of unqualified applicants – resulting in
overlooked candidates, expensive mis-hires, and less time to pursue quality leads. In one interview with a Fortune
100 enterprise company, we learned that of the 2.5 million resumes his company receives annually, only 30% are
viewed. We plan to eliminate this noise by leveraging data science techniques to augment hiring teams.
While we’ve seen a lot of interest from employers, our focus is currently on building the job seeker side of the
market. UpScored’s algorithms have been trained on over 500,000 resumes, and we recently launched our public
beta to job seekers with over 16,000 job descriptions on the site. Currently, we’re focused on the New York area
with plans to expand to other regions in the future.
Over the next 6 months, UpScored will be focused on onboarding high-quality candidates to the platform, expanding
to additional geographies, and adding functionality based on user needs and feedback. We’re also developing a
resume analytics tool that we will be releasing to our job seekers soon. This tool will give visibility into our ranking algorithm, so users will have the transparency they need to get to the job they really want.
Which Industries do you believe will be the pioneers in broadly adopting AI technologies? Is HR/Recruitment one of them?
We see a massive opportunity for AI technologies in the HR/Recruitment space. McKinsey predicts that online
talent platforms will add $2.7 trillion to global GDP by 2025, yet only 4% of large organizations have any ability to “predict” or “model” their workforce. The labor market is in desperate need of more advanced technology. More
specifically, we’ve spoken to over 120 recruiting leaders at Fortune 500 companies, startups, and recruiting firms – the majority of which are searching for better data solutions to recruit and hire. The market for “people analytics” will grow significantly as data science and AI technologies mature.
Elise Runde Voss, CEO & Co-Founder and Dan Elbaz, CTO & Co-Founder of Upscored
How do you see the Enterprise AI market evolving over the next 5 years?
While AI is still often seen as a technology of the distant future for many aside from the tech behemoths, we’re
beginning to see cognitive computing and machine learning become a focus for smaller and mid-sized companies
across all departments. We believe that within 5 years, machine learning will aid companies of all sizes in every
aspect such as cost cutting, building better products, and optimizing sales strategies. This shift will be made possible due to 1) more valuable data collection; 2) new algorithms that are more efficient for analyzing and modeling this data; and 3) an increasing supply of data science and machine learning skills in the labor market. While currently these skills are at a deficit, supply will eventually catch-up to demand as academic programs and courses adapt.
For example, we recently ran a study based on the half a million resumes and thousands of open job descriptions in our system with the goal of analyzing the top “skill gaps” among technical roles. We recognize “skill gaps” as skills that appear proportionally more frequently in job descriptions than in resumes. Demand for skills associated with NoSQL technologies has clearly outpaced supply. “NoSQL,” “Redis,” “Hadoop,” “Cassandra,” and “MongoDB” were among the top 15 scarcest skills. As data science becomes (and is) integral, we expect companies of all sizes to start accelerating requirements for skills like “Machine Learning,” “Data Science,” and “Statistical Modeling.” We’re already seeing the trend. We recently wrote a piece on it in Capitalize on the Most Sought-After Technical Skills.
For more information on Upscored and a trial of the service visit their website!