Ilit Raz, Founder and CEO of Joonko – Interview Sequence



Ilit Raz is the founder and CEO of Joonko, a platform that helps companies apply AI to their range sourcing technique. Immediately her firm works with Adidas, American Categorical, Crocs and PayPal. She’s raised over $38.5M and the corporate has grown 500% for 2 consecutive years.

What initially attracted you to pc science?

Know-how is among the largest and most profitable industries in Israel, so I’ve all the time been uncovered to the business in a technique or one other all through my life. Once I entered the military, I earned the chance to work in a know-how unit the place I managed the event of safety software program and frolicked studying about pc science. From there I used to be hooked and knew I needed to pursue it as a profession as soon as I left the military.

When did you initially grow to be uncovered to varied gaps within the business comparable to wage and promotional gaps?

Throughout my first couple of years working at non-public software program firms, I wasn’t personally conscious of the bias girls confronted. Then, I began to community with technologists that occurred to be girls. I rapidly grew to become conscious of how massive the issue was after listening to the tales these girls shared about being talked over, ignored, or not getting credit score for his or her concepts.

Are you able to share the genesis story behind Joonko?

I’ve a level in pc science and a background in software program engineering and NLP. I’ve personally skilled each unconscious, and acutely aware, bias by my skilled environment, and a bunch of feminine product managers I used to be part of additionally uncovered me to office points that have been extra than simply wage gaps. This appears like conferences getting scheduled when girls or mother and father want to depart work or witnessing who will get to speak or current throughout conferences. Though these situations appear minor, they’re important and influential while you’re the particular person being impacted.

I got here to know this was a extra widespread downside, so I made a decision to make use of my technical background––I’ve a level in CS and a background in software program engineering and NLP––and deal with it head-on by creating a brand new know-how answer, which is how Joonko was born.

How does Joonko supply the expertise pool of candidates from various and underrepresented backgrounds?

Our proprietary algorithm first makes use of pure language processing and pc imaginative and prescient to scan public information on the candidates which are referred to us. We search for information that validates whether or not somebody self identifies as underrepresented. For instance, if an individual has “she/her” pronouns on their LinkedIn, we will infer that they could self establish as a girl and assign that information level some extent. If the particular person’s profile collects sufficient factors, we invite them to our expertise community, and as soon as they enroll, they additional validate our assumption by telling us how they establish.

How does Joonko then vet this expertise?

We use a mixture of human contact and know-how to match candidates with the open positions which are a match. First, every candidate that joins our community is referred by the hiring crew they lately interviewed with, however couldn’t rent them. The hiring groups solely refer candidates that made it to the ultimate spherical thus making certain they’re prime quality candidates. From there, we use pure language processing to match the candidate with the corporate and function that’s the proper match. We acquire key phrases from their resume and the function they initially interviewed for, then examine that with the roles marketed on our platform. Most fashions solely use two information units, so utilizing three as an alternative will increase our means to make the appropriate match.

How does Joonko help firms with retaining this expertise?

We help firms in retaining expertise all through the recruiting course of by integrating with the applicant monitoring system. Our integration permits us to drag information, in combination, about how far Joonko candidates get by the pipeline. Wherever we see a drop off compared to non-Joonko candidates, we work with firms to both enhance the matching or enhance their recruitment course of.

What are another ways in which Joonko makes use of AI in its hiring or match making course of?

We leverage pc imaginative and prescient and pure language processing to find out whether or not a candidate self-identifies as underrepresented. We use pure language processing to match candidates with the roles in our pool and we use machine studying to enhance the matching course of as candidates choose the roles they’re interested by. Lastly, the matching and referral is automated from finish to finish. Recruiters don’t should do something till they resolve to interview a candidate referred by Joonko.

Might you focus on the advantages of a diversified hiring pool to keep away from AI bias?

The way in which we have a look at it’s, the extra underrepresented candidates you’ll be able to appeal to and interview, the extra information you’ll be able to audit for human and technological bias. Bias, at its core, happens when a mannequin (or particular person) is used to seeing comparable information again and again. While you closely put money into candidate range you’ll be able to prepare your know-how, and the recruiting crew that makes use of it, to contribute to the range flywheel.

What are another causes range must be a precedence for firms?

A lot of firms usually depend on referrals to fill open roles, which information exhibits can result in a homogeneous workforce. I consider it’s vital for firms to place a highlight on missed expertise – together with ‘silver medalist candidates’ who made it to the ultimate levels at prime firms however didn’t find yourself getting the job.

Not solely is prioritizing DE&I objectively the truthful and proper factor to do and an vital a part of a forward-thinking, equitable society, however it’s additionally merely good for enterprise – firms that prioritize these efforts are extra productive and profitable, whereas workers are happier and stick round longer.

Do you have got any remaining recommendation for ladies who’re taking a look at leaping in pc science or AI?

Discover communities of girls you’ll be able to lean on when issues get robust. The way forward for the factitious intelligence business will depend on the participation of girls, however is at present dominated by males. The sooner you’ll be able to construct a community of girls who share your experiences, the extra possible you might be to be supported and thrive within the business.

Thanks for the nice interview, readers who want to be taught extra ought to go to Joonko.