Yobs.io isn’t the first HR tech company to promise better candidate selection technology through AI and predictive analytics. HireVue has been using algorithms to review and assess video interviews for companies like Unilever and JP Morgan, and they’ve got $93 million in funding to do it. AI technology is rapidly changing the job search.
Yobs.io, however, positions itself as a platform that can identify a candidate’s soft skills and improve team dynamics. Their tech implements “quantiative soft skills analysis in the recruitment.” It claims its platform “determines the emotional state of your candidate which reflect the real-time soft skills that they will take to the job everyday.” Their algorithms analyze facial expressions, word choice and tone, and even head speed to predict candidate success in an organization.
I find it hilarious that employers are banging the drums about the need for employees with soft skills yet they’re increasingly willing to hand over the process of selecting people with those same skills to a machine.
I work on interview chatbots and conversational AI in my contract work. I find it fascinating. I enjoy watching the algorithm improve and seeing its limitations. However, technology that uses personality assessments and predictive analytics to make hiring decisions fills me with questions. They’re questions that I rarely see addressed in tech media or HR industry coverage. They’re questions in need of answers that aren’t marketing copy.
Just look at that engagement level! Source: Yobs.io website
Here’s the ongoing list of questions I never see answers to:
How are companies evaluating whether hires by AI are better than human-led hires? Is this technology trusted for use in all hires, including executive management? Moreover, do the AI engineers have the soft skills they’re designing algorithms for? Does it matter if they don’t? Do the managers who oversee the implementation of this technology also have the soft skills they seek?
Also…
Why should my head speed be part of my interview evaluation? How much weight is my head speed given in the algorithm? What is a quality head speed and how does it affect my ability to do a job that I’ve trained for? Who decides what interview tone is appropriate? Would a monotone AI engineer with an abnormal head speed, a high rate of neuroticism, low rate of extraversion be an acceptable hire (trick question, of course they would, they’re the most in-demand occupation)
And…
Who loses out on an opportunity during the tuning phase of the algorithm? Algorithms don’t work perfectly out of the gate. What feedback loops exist inside the organization’s that use this tech to ensure they’re not getting false negatives? How do HR tech companies who claim to reduce bias prove they actual reduce bias rather than reinforce it?
Humans are flawed. But so are algorithms and even the data we use to build them. Just because it can be measured (head speed) doesn’t mean it needs to be. Asking the hard questions about new technology is important, especially in high stakes situations like job interviews and career progression.
Also, I’m parking this fab find here: Yobs.io uses the big 5 personality traits (OCEAN) to predict candidate fit. There’s a fabulous overview of the Big 5 that includes psych student videos explaining the big 5 concepts. Highly recommend watching these videos, especially when they discuss the person-situation debate.
In my last role I talked with MBA recruiters about their hiring needs on the regular. When I asked what they were looking for in a candidate the most common answer was: people that can work with data. The need for data-savvy candidates spanned industries and roles. An MBA doesn’t guarantee someone has experience working with data. At the time MBAs were still trying to upgrade their curriculum to include this skill. Yet overwhelmingly hiring managers wanted people who understood how to work with data. These conversations happened in 2016. Now the need is even greater.
Data powers modern organizations. Your ability to identify relevant data, evaluate it, work with it, and communicate what actions to take based on it, is crucial to staying relevant in the business world. And this isn’t just for MBAs – this goes for anyone working in a business organization.
Thankfully you don’t have to be a data scientist to work with data. There are plenty of data-based opportunities that aren’t as hardcore as a data scientist. Some of those opportunities are summed up nicely in this HBR post, You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role
Companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and — perhaps most important — translators.
Data translators are exactly what they sound like: people who can translate data into meaning. These are the employees who bridge the “technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers.” They’re natural communicators and collaborators. They adapt and understand business goals across teams. Data translators have major soft skills with a solid foundation in analytics. They’re are also highly employable. IBM estimates that by 2020 over 2 million analytics roles will need to be filled. Those organizations are going to need a shitton of data translators.
According to the HBR article above, the best hires come from inside the organization. This means you’ve got a chance at positioning yourself for this future-proof role.
If you’re not using data in your current job you have two options: find another role so your skills remain relevant or create your own data translator role within your department. This is a new, evolving role. Data translators may not currently exist in your organization. Or they may exist but operate under a different job title.
Prepare for the role by exploring opportunities inside your organization to work with data. Get to know your data science team (if there is one). Start a conversation with your boss about your involvement in data-driven projects. Ask about the departments goals. Ask which data is already analyzed and used to support business goals. Identify which data-driven projects exist on your team and then find a way to get involved or at least shadow the project. Create your own data viz project by watching YouTube videos about Tableau and using relevant data from your department. Present to your team about your findings. Then identify a department that you collaborate with regularly. Get to know their business goals and how they work with data to make strategic decisions. The ideal data translator works seamlessly across departments. Getting to know the people in other departments – as well as their business goals – will position you well for any data translation job. Also, you can supplement all of this with online courses. Coursera and FutureLearn have excellent options.
Your ability to work with data is a must-have skill. You need it if you want to move up. But you also need the skill to ensure your relevance in the next 5 years of workplace evolution. If you don’t have the skills and experience to work with data this is the time to start upskilling and adding data analytics to your skill collection.
Here are two brutal quotes from an Axios post reporting on executives’ attitudes towards general pay raises and employee retraining. There were made during a conference for CEOs titled “Technology-Enabled Disruption: Implications for Business, Labor Markets, and Monetary Policy.”
“Executives of big U.S. companies suggest that the days of most people getting a pay raise are over, and that they also plan to reduce their work forces further.”
Damn. And then:
The moderator asked the panel whether there would be broad-based wage gains again. “It’s just not going to happen,” Taylor said. The gains would go mostly to technically-skilled employees, he said. As for a general raise? “Absolutely not in my business,” he said.
The CFO of AT&T also said that he doesn’t have a need for so many call center employees or guys that install their cables.
The message is pretty clear: employers don’t need you.
The idea that employees should be loyal to companies is a hold over from traditional career narratives. We’re still waiting for old school career narratives to catch up the present reality of work. But in the meantime it’s a good reminder that companies aren’t looking out for your best professional interest. Waiting for your employer to give you a raise, direct you to the next step, or reward you for your hard work – that’s not going to happen. Instead, it’s going to be up to you to figure out your next move and make sure you have the skills to get a pay upgrade. Don’t expect your employer to do it.
There’s a lot of bad career advice masquerading as good advice. Much of it stems from outdated notions about careers. Advice like “stick with a job at least two years” and “don’t job hop, it’ll hurt your resume!” is meant for old school careers where companies invested in employees. It was meant for a time when people stayed with companies 5, 10, even 15 (!) years.
This advice is dead wrong.
It keeps people in miserable jobs.
And there’s no need for it in the new world of work.
This perspective was most expertly summed up in the tweet thread below:
My first job in tech, I cried every day but stayed b/c I worried leaving at 4mos would look bad on a resume.
6 years later when it happened again, I left immediately.
Both times I moved to better companies that valued & supported me. Don’t stay somewhere shitty for your resume.
If you’ve got a bad manager or work in a toxic environment, leave. I don’t care if you’re two months into a new job, if you have the means to leave, gtfo. Don’t waste your time because it’ll look bad on your resume. Don’t stick with it to tough it out. It’s not worth your time or sanity, especially if you’re earlier in your career. It’s totally ok to make a mistake. (Note: not everyone has the means to escape; this is advice for those who do)
Instead, put all your energy into leaving asap. Build a story that explains the honest reasons why you left (bad work culture is a perfectly ok reason to leave). Build relationships with people inside companies that are known for having good work cultures. Learn what you like in a manager. Ask people what their managers are like during careful informational interviewing. Read Glassdoor reviews.
But don’t stay at shitty jobs just because of the fear of being perceived as a job hopper. With the number of workers who work in the gig economy, the increase of job seekers with side hustles, a tight labor market, new job types, there’s a lot more fluidity in your career. Employers can work with job hoppers. It’s not worth it to stay.
So hey, if you’re in this position, start plotting your escape.
What tweaks could we make to the college curriculum that would help students prepare for the changing workforce? This quote from the article, The Global University Employability Ranking 2017, at the Times Higher Education, offers a clever solution:
“The way organisations have to work these days needs to be very fluid. In that kind of world it is important to have people who are really flexible, able to create networks within their organisations and very comfortable working in virtual teams and particularly [what we call] leading beyond authority: not necessarily having to get things done because they are in a team that has a boss,” he says.
But he is “not sure” that the implications of this are “well understood by the academic world and, therefore, when we throw a new graduate into [work] it can be quite overwhelming [for the graduate]”. One solution, he suggests, is for university courses to have more group projects, with assessment focused on the process that the participants go through, rather than the outcome.
Flourishing in such an environment requires “reflection and understanding”, and especially learning from mistakes, Saha says. He is sceptical that this aspect of professional competence is well explored in universities currently, but “in the working world, that is the bit that can be make or break”.
He’s spot on in his assessment and solution. Focusing on group work and assessing participants on their process, instead of outcomes, could go a long way to help students identify their strengths, weaknesses, and improve their leadership and collaboration skills. What really struck me in that sentence is that focusing on process, rather than outcomes, is the opposite of American business culture. American learning and working culture is focused specifically on outcomes – we’re obsessed with assessing programs. Managers evaluate employees based on their results, not collaboration.
I’ve never in my work life been on a team that was evaluated on how well they worked on a project together. It’s almost a revolutionary suggestion.
Brent (a pseudonym) is 52, his youthful appearance the result of rhinoplasty and a modified lower face-lift. He took a week off from a previous job to get the surgery. “Knowing I’m going back in to fight for another two or three jobs and that I’m going to be surrounded by a bunch of thirtysomethings,” he says, “my take was: I don’t have a problem looking 10 or 15 years younger than I am.” – The Brotox Boom: Why More Men Are Turning to Plastic Surgery
The term career seems so quaint when you read a phrase like “fight for another two or three jobs” from a 52 year old. Age discrimination is real and I feel for these bros or anyone who has to compete with 30 year olds to remain relevant. Maybe we shouldn’t fear the robots so much as the youth.
And maybe tech should start including plastic surgery as part of the benefit package to help them win the war for talent.
Yesterday I presented to a group of undergraduate students at PSU about the future of work and the coming changes to the workforce. As someone who regularly talks about the future of work this was the first time I’ve stood in front of soon-to-graduate students and tell them they’ll need to become lifelong learners because artificial intelligence. It’s a bit of an awkward message to deliver. They’re in their last term, weeks aways from finishing up four years of learning, working, and preparing for their next career move. They are ready to take on the world with their new skills. And I’m telling them they’re going to need to keep learning, upskilling, post-college.
But the students were game for the discussion and asked solid questions.
The experience, however, highlights one of the biggest challenges I have right now. Everyone working in future of work spaces is working to educate employees and students about the coming changes to the workforce. Despite the blazing headlines about robots taking our jobs, the subject (or fear?) isn’t tangible enough to stick. How do we get people to shift from outdated career models and thinking to commit to lifelong learning and upskilling? How do we get people to see how artificial intelligence is changing the workplace and our jobs, if they aren’t yet feeling affecting by the technology?
Yesterday I asked if any of the students had experienced an interview using the HireVue platform. One had. I asked if she knew she was being evaluated by algorithms. She responded that she wasn’t, and the audible, “Whaaaat?” and gasps from the audience indicated most students weren’t aware either. Job seekers need to know about the technology that’s being used to evaluate them.
For yesterday’s talk I put together the resources to help students understand the coming changes, the technology, and how to prepare for an ambiguous career. If you’ve seen the headlines about robots taking our jobs and want to get beyond the headline hype, check out the resources below.
Start with the video below as an introduction to the subject.
BONUS WATCHING: Learn about the digital skills gap
It’s something that has been a bit of a mantra in the educational field. Everyone is going to have to be a student for life and embark on lifelong learning. The fact is right now it’s still mainly a slogan. Even within jobs and companies there’s not lifelong training. In fact what we see in corporate training data at least in the United States, is that companies are spending less. As we know right now people expect that they get their education in the early 20s or late 20s and then they’re done. They’re going to go off and work for 40, 50 years. And that model of getting education up front and working for many decades, without ever going through formal or informal training again is clearly not going to be the reality for the next generation.
Continuing on that theme is another article by McKinsey, Getting Ready for the Future of Work, which is worth reading if only for this shocking quote right here:
The time it takes for people’s skills to become irrelevant will shrink. It used to be, “I got my skills in my 20s; I can hang on until 60.” It’s not going to be like that anymore. We’re going to live in an era of people finding their skills irrelevant at age 45, 40, 35. And there are going to be a great many people who are out of work.
I’m a liberal arts grad. I love words and language. I teach soft skills. Qualitative data is my jam. I’m also obsessed with machine learning (ML) and artificial intelligence (AI).
In 2015 I tumbled down the AI rabbit hole after discovering a long read on the fabulous site Wait But Why. The site explains complex ideas paired with hilarious stick figures. The two part series on AI, The Artificial Intelligence Revolution, was my gateway article to the world of AI, and later ML as part of AI.
So far my self-directed learning journey has only included reading about AI and writing about its affectonhiring and the future of work. I can’t code in Python (with zero plans to do anything with R). My data background includes data analytics, cleaning data, and putting it into Tableau but nothing close to data scientist. I also have no interest in going that far professionally. As a non-tech person trying to access ML/AI, it’s been a challenge to figure out where I fit in. I’ve uncharacteristically avoided meetup groups or conferences on the subject since I don’t have the tech skills.
Not me.
Last month I changed that. I got tired of reading. I wanted idea exchanges. So I attended a ML/Al unconference in PDX. And hot damn I found my people!
An unconference is the opposite of the standard conference setup. Instead of corporate-sponsored keynotes paired with bland chicken and an abundance of shy speakers who read PowerPoints, the participants chose the content. We pitched and voted on what they wanted to talk about. The result was facilitated conversations about subjects we were curious about and a format that flowed. It was the ideal setup for idea exchange and learning.If you’re conference weary an unconference will restore your faith in professional development.
Many people at the unconference were data scientists or computer scientists, and some working on ML projects. A few were students or job seekers. I met one other person who is like me, a communications expert without a technical background who works for a machine learning platform, BigML (and they’re doing rad stuff).
In our sessions we covered a roving range of topics about ML/AI: novel data sets, making AI more accessible to the masses, establishing trust with users, data security, AI decision making re: self-driving cars and the Arizona accident, becoming a data scientist and machine learning engineer, the future of companies and jobs (my pitch!), learning ML/AI as a new person (do you learn the math, the code, or find a project first? plenty of debate on this!), and plenty more side conversations that spilled out of the main sessions.
As an non-tech outsider it’s a bit intimidating to participate in such a cutting-edge tech space. I think ML/AI people forget that at times. One of the guys I met at the conference noted that when you’re an expert it’s hard to remember how hard it is for others to start in your field. I’ll add that this goes double if you’re in a quant and code heavy field like machine learning. Luckily most everyone at the unconference made it easy to participate (as did the unconference format).
My main takeaway though is that you don’t need to be a software engineer, data science expert, or code wizard to understand ML/AI.
So for all the people who are curious about ML/AI but don’t know how to start engaging in these communities, here’s how.
Learn the basics: Know the difference between machine learning and AI; understand the difference between Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence; understand the basics of data science. There are no shortage of intro articles and videos on the subject (two examples below).
Prior to the uconference I was slightly worried I’d be left out of the conversation if it turned to technical. I prepared by returning to a set a YouTube videos I’d skimmed a while back: Fun and Easy Machine Learning. The YouTube list animates over 15 models to better understand machine learning.
Ignore the math and coding right now: Unless you want to become a data scientist or machine learning engineer, ignore it. You don’t need it to understand the basics or to explore products or impacts of ML/AI. For example, the Fun and Easy Machine Learning series sometimes dives into the math behind the models. Treat it as you would a foreign language; when you don’t the meaning keep moving forward and focus on what you do understand. Fill in the blanks later.
Read everything about ML/AI in the area you’re interested in. ML/AI for non tech people is a huge field. So narrow it down. Start with general articles about artificial intelligence and learn about it’s expected impact. The World Economic Forum has good articles with a global perspective. For business impacts, check out this history of ML/AI technology by industry/verticals. Then head over to CB Insights to study ML/AI companies (and subscribe to their newsletter as they’re cutting edge everything). Then pick an industry that interests you. Either one that you work in or one that you want to work in. Read everything you can about how machine learning is affecting that industry (it’s affecting all of them – right now finance, healthcare, and insurance are some of the industries talked about the most.) Explore products and platforms in that industry that use ML/AI. Read case studies. I study the future of work. So I read everything I can about ML/AI and it’s affect on workers and organizations: McKinsey, AXIOS, MIT, plus I play with HR Tech.
Avoid the hype. It’s easy to get caught up in the shiny promised of AI. Instead, pay attention to counter narratives, often published outside of the tech reporting ecosystem. Find the counter narrative about AI in your field. I read the amazing research and work by Audrey Waters at Hack Education for a counter narrative to AI edtech hype. Explore bias in ML/AI. Understand how AI isn’t neutral and that gender and race bias is coded into AI systems. Weapons of Math Destruction is an excellent book (and 99% Design has a good podcast on it). We need diverse perspectives and people in ML/AI fields to fight these bias, and non-technical people are part of that fight.
Take a course: FutureLearn, an online learning platform with a name after my own heart, offers an Intro to Data Mining course where you’ll learn the basics of classification algorithms. It’s a smooth intro to applied machine learning. They also offer an advanced course to build your skills further.
Go to an event and talk to people: This is the intimidating part. But get over it, embrace the awkwardness, and commit to asking curious questions. Remind yourself of the things that you know. Write down the things that you want to learn. Talk to people until you get the answers to your questions. Ask people how they got into their work, what impact they’re having, and how they’d explain their work to a non tech person. Tell them you’re curious. Some people will just talk at you. Others will teach you. Keep in touch with the people who teach you and simply move on from the ones who talk at you.
Get a project: This builds on not worrying about the math and coding. Instead, get a project. What problem do you want to solve? What problem does your organization need to solve? What data is available? What data is missing? How could ML/AI solve your problem? Starting there will help you lead you in the right direction. You might not have an answer right away. That’s ok. It make take a while to solve it. But that’s the point. You’re learning. Ambiguity is part of the process. So ask around your workplace. Visit the data science or computer science team in your organization (assuming you have one). Find a data scientist in your network or at ML/AI events and ask them how they’d solve your problem. Ask them to break it down. Ask a computer science student what they think.
Start with curiosity, ignore the part about not having a technical background, and see where it takes you.
I’m still on an HR Tech deep dive. This time I found a remarkable platform that takes a proactive approach to employee referrals. Teamable helps employees make referrals and reach out to their contacts for opportunities. They do it by mining current employees’ social contacts and building profiles of potential candidates.
Here’s how it works:
This is even more motivation to connect with people: build relationships and get discovered.
I’m still conflicted about all the HR Tech that creeps on you. There’s a great deal of social scraping going on across HR Tech. But at least this platforms helps existing employees improve their referrals (and get money) and helps people who are actively building relationships get seen and hopefully hired.
Here’s a little more on what Teamable is up to and what they’ll do with their $5 mil round of funding that they don’t need.
BONUS: The founder’s badass bio mentioned rugby and travel, which is basically the greatest:
Rugby and travel also taught me everything I need to know about business.
For low-paid contractors who do the grunt work for big tech companies, the incentive to keep silent is more stick than carrot. What they lack in stock options and a sense of corporate tribalism, they make up for in fear of losing their jobs. One European Facebook content moderator signed a contract, seen by the Guardian, which granted the company the right to monitor and record his social media activities, including his personal Facebook account, as well as emails, phone calls and internet use. He also agreed to random personal searches of his belongings including bags, briefcases and car while on company premises. Refusal to allow such searches would be treated as gross misconduct.
There are some truly shitty practices happening at top technology companies like Facebook and Google. The paranoia is so bad in some companies that “some employees switch their phones off or hide them out of fear that their location is being tracked.”
So how does a job seeker know to avoid companies that treat their employers like this? And does it even matter because the long term benefits of getting Facebook or Google on your resume and working on cutting edge projects outweigh the risks of daily corporate surveillance? (yes, it should matter, but try telling that to a new graduate)
