Driving away great, qualified candidates is the ultimate facepalm
You may not want to hear it. You may cover your ears and
hide from it. You may avoid anyone who is trying to tell you this, but you have
to face facts. When it comes to AI, Machine Learning and Data Science, quality
candidates are currently swamped with offers. This is no longer a buyer’s
market.
Yet still businesses are acting like they have all the time
in the world to find the skilled individuals or teams that will make their data
projects happen. One could put it down to burying their heads in the ground,
one could allow for the company being slow to identify and fix problems, one
could consider that to some, this is just how recruitment is: a long drawn-out
experience.
But let’s imagine your firm doesn’t actually want to lose
prime candidates, causing work and projects to back up, money draining away.
Let’s imagine that you don’t have that much time to waste. How can you avoid
this happening to you?
Well, firstly, you need to actually acknowledge that you
have a gut. We’re not talking about any lockdown weight you might have put on.
We are talking about that sense you have when something is wrong or right.
Without using your gut, especially within data recruitment, you are in for a
world of pain.
Look, we understand that Covid-19 has complicated hiring.
Doing video interviews can be a pain and they can really take some getting used
to. Poor internet connection or worrying about the webcam that is pointing
right up your nose can distract you from taking in everything the prospect is
saying. This can only improve with practice.
Do you know how well some companies are doing at hiring
during these times? One company we know of makes offers after one video
interview. How can this be? Because they are organised, they are cool and they
are going with their gut when it comes to the hire. Ask yourself: would you or
could you forgive a bad video interview? Would you display the same courtesy to
the candidate that they would to you if you were having technical issues or
were nervous about whether the room they are speaking to you from looks a mess
or not?
Without understanding, and gut instinct, this process will
not be easy on you. It is a fact, in many cases, that businesses are over-interviewing
because they can’t interview in the office. Is it feasible to do face-to-face?
With lockdown easing, there are workarounds, as long as you bear in mind the
safety concerns.
The amount of stages within the interviews are growing, due
mainly to hesitancy to just make a decision. What do you think is reasonable?
When you were at school, did you love taking tests? Well guess what? Adults
don’t like it either. Sometimes you have to make a candidate take a test and
that is fair enough, but multiple tests, no matter the purpose, feel excessive
for the candidate. For candidates receiving multiple offers, it is downright
crazy to do this.
What does it say about your company when your competitor can
wrap up the process in two steps? Your lack of consideration for your processes
is killing candidates’ perception of your organisation
Take some responsibility. Don’t pass the buck by saying “Oh
I like him/her but what do you think?” Why should a candidate sign up for a job
where the managers refuse to commit to a course of action? It is fair to say
that the prospect, when imagining what it would be like to work for you, will
not imagine a company that can deliver on its promises even if it really wants
to.
Long-winded and inflexible hiring practices lead to a paralysis
of projects which leads to desperation further down the line. While you are
holding off on hiring, waiting for that imaginary, perfect candidate you
haven’t even met yet, the great candidates you have met will lose patience with
you and go elsewhere.
So what are you going to do about it?
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
AI has its uses but it should not be treated as a solution for everything
Whilst we recruit Machine Learning and Data experts for other companies, the actual use of Machine Learning for recruiting purposes is highly problematic. It may seem crazy that recruiters working within this sector would question ML, but because we know this subject, it makes perfect sense to us.
Yes, technology helps us recruiter in some ways, but the
idea of a one-size-fits-all solution is the incorrect reading of the situation.
Tech can help us find new ways to reach out to the right candidates but when it
comes to finding and selecting who is perfect for the role, it lacks the
natural element, that understanding that comes with interacting with those
kinds of candidates, working with those businesses, researching the roles. It
lacks a human touch, that extra sense that is sometimes indefinable.
Would you buy a house with no human interaction whatsoever?
Yes you can look at the pretty pictures, you can even walk around it. But if no
one was talking to you about the house, if you couldn’t meet the owners and
gauge them, if you couldn’t get a sense of what it is like to actually live
there, something just wouldn’t feel right. Finding a job, or finding the right
individual to fill a role, is a really big deal. No one goes into it lightly
and rightfully so. Selections made via automated algorithm can seem right but
scratching beneath the surface reveals a more complicated situation.
The idea that most people have, and one which drives the
concept of using AI to dominate the recruiting process, is that machines have
no bias. It is true that machines do not have emotions and if that were the
only factor, then it would be understandable that you would take that route
with your recruitment. The problem is that machines need to be fed facts by
humans. Biased humans equal biased data equal biased AI decisions.
The Amazon gender bias story from 2018 highlights this.
Amazon were seeking a system to simplify the process, where they could input a
lot of CVs and the machine would select the top five based on a rating system.
The problem was that the machine could only base its future predictions on past
actions. Amazon realised that, because the past ten years had been dominated by
male applicants, the AI had a bias against female applicants.
The Amazon debacle happened with correctly inputted data. If
the data is inputted incorrectly or is formatted in a way that the system
cannot read it, that information will be missing from the final decision. There
is no universal format for a resume, so you can imagine the problems that could
ensue there. Sometimes people just are not very good at writing their CV. They
may miss out something that the AI is searching for. There may be a spelling
error. They may possess extraordinary skills but few qualifications. The AI can
only view the information in a way that it has been instructed to. It is
fascinating when you start to think of all of the things you can pick up from a
resume instinctively, and if you had to tell someone else your thought process,
you would be there for a week, because you would have to impart the stories of
all the place and situations in which you learned those things. Suffice it to
say, there isn’t enough time to teach a machine what is inside your head.
And all of that is before you even get to the emotionless impersonality
of it all, the lack of understanding when it comes to personality and the way
machines struggle to process the ever-evolving and complicated rules of slang.
Here at Zenshin Talent we embrace AI and ML to help us target the right candidates in a more efficient way. The other element we always include is our human understanding, using it to nurture the client and candidate relationships in order to ensure the correct match.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
It should be a real connection, not just a contract
With a changing job market, there comes a changing
recruitment landscape. While there are still cowboys out there, savvy
businesses are engaging recruitment partners in exclusive contracts. This may
seem from the outside a more expensive option, but when you scratch beneath the
surface, it becomes apparent that this approach can save time, money and
effort. How can this be?
Let’s first pick apart what the phrase ‘Exclusive
Recruitment Partnership’ means. ‘Exclusive’ refers to the fact that one
recruiter is tasked with finding the best candidates for your project, relying
on an agreement that means they are the only recruiter you engage, for a set
time period. ‘Partnership’ means that the recruiter is involved in more than
just finding the prospects. They are invested in the success and may take on an
advisory role during the hiring too, recommending hiring strategy or rewriting
job specs.
The benefits can be boiled down to four words:
Accountability, Security, Expertise and Empathy.
A contract is signed and will be honoured. Because there is
a higher level of accountability and responsibility than would be placed on a
group of generalist recruiters, you are more likely to get the right result.
That recruitment partner you have engaged takes seriously
the fact that they are representing your company. They have worked in this role
for many years and as they are specialising, it means they have had success
within their niche. If they had not, they would very likely be a generalist
recruiter and would not be involved in this conversation.
The recruiter will use their expertise to ask questions
about what you are looking for, past experiences and which skills are ‘nice to
haves’ and which ones are ‘absolute needs’. That expertise comes in handy when
persuading candidates who are not actively seeking a change, that the company
knows what it is doing and that they are a perfect match.
It is about finding the best fit for your company and with
empathy and understanding, they are the only ones who understand your business,
understand the sector and know where to find numerous candidates who could fit.
Without the desperation involved in generalism, they can select only the best
prospects, saving that company time and money. The recruitment partner is ready
to give you a breakdown of why this individual is a great fit for the role.
They do not just disappear into the ether and emerge to give you some CVs. They
are working to agreed timescales and update you regularly.
This does not have to be a long-term contract or some kind
of inflexible arrangement. The idea is that the situation suits both the
company looking to hire and the recruitment partner. Some partnerships last
four weeks, which is usually long enough to find an amazing candidate to fill
one role. Some partnerships can be on a rolling basis. Generally, they involve
targets to meet, whether they are stage-based or result-based. Some choose to
have a triggering of a percentage of the payment when candidates reach the
selection stage, the interview stage and the hiring stage. This is not set in stone.
Some may receive their fee when everything is settled. Some may have a
cancellation clause fee in order to make sure their valuable time has not been
wasted. There are many options and at the end of the day, without mutual
respect and without top results, no quality hiring will occur.
There are many other benefits too, including enhanced
engagement, better value and the fact that, for sensitive hires, the
relationship guarantees confidentiality and discretion.
So when you think about your next vacancy, consider that the ‘spray-and-pray’ approach may not be the right solution. If you have struggled to fill job roles in the past, was it because great candidates weren’t around, or was it the methods used that caused the problem? It may be time to try something new.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
Data. Everyone is at it. And you should probably do it too. Before
that happens you need to understand where the data is coming from, what it can
tell you, how it can make a real impact on your business and how you are going
to start the process.
Things can grind to a halt when the AI you employ tries to
work with incomplete data, low quality data or badly structured data. If you
start the more advanced part of the process before you start collecting and
cleaning data, working out what data and why you need it and what you will need
it for, then you are just throwing resources away and your timelines will start
to stretch further and further into the future.
There is a lot of pressure for companies to start running to
the finish line, spooked by their competitors, but in this story you will want
to be the tortoise rather than the hare, taking it steady at first, to make
sure you have the pieces you need in place before moving onto the latter
stages.
Hiring a team before you have an infrastructure plan in
place is madness. Fresh data scientists being thrown in at the deep end will
not only fail at a thankless task, but they will become alienated, the project
will fall behind, the delays will drain more budget and you will be where you
started: a company unable to mine value from its data.
It also runs the risk of souring the C-Suite on the idea of
data as a worthwhile pursuit. They threw money at the problem, data people were
hired, but there is now nothing to show for it. What gives?
To avoid this frustration, a clear understanding must be
sought about who is needed and at what stages. Specialist recruiters can be
brought in to build a team from the ground up, with realistic time frames, and a
knowledge of who is needed when. They will understand that instead of cramming
a job spec with every skill you can think of, the description should be
targeted for the specific role. There are a diverse set of skills out there in
the workforce to be tapped into. People who could be perfect for the task you
require should not be dismissed out of hand. If you are requiring someone have
a number of years’ experience in something which, until 5 years ago, was
virtually unknown, then you are damaging your chances of filling that role.
Before you have an answer, you must have a question, right?
That is how it works in the world and it is also how it works within Data.
Sometimes it is a ‘chicken and the egg’ situation: figuring out what you need
done, then working out if you have, or can obtain, the data to get it done, or
figuring out the data and working out whether it has value for your future.
Either way, this groundwork must be completed before the hiring of the full
team. You may hire in a consultant to create the plan, but it cannot be fully
left to those with no, or very little, data experience. This is a time when
core business problems are discussed and evaluated, while data possibilities
are floated.
A good starting point is to educate everyone within the
company about the importance of data. This can help, as the suggestions of the
staff themselves, when it comes to problems that can conceivably be solved by
data, can really drive change.
Understanding that data can be cross-departmental is another
step forward. There is a habit to view this as just an IT issue, but data
should be coming from multiple departments within your company, so there needs
to be cooperation in order to make sure it is correctly generated and stored
and can be used in a timely fashion.
Knowing where your data is actually stored and what kind of
state it is in, is a great start. So many companies have it stored hither and
thither, or just plain don’t know where it is because they have not had to
think about it since it was created.
At all stages there should be an understanding that this will positively impact the business in real terms. There must be business value in the data if this is to be a worthwhile endeavour.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
Around 5 years ago, ‘Data Scientist’ became part of the
public subconscious and everyone was talking about it. Dashboards and 3D
visualisations were flying around left, right and centre. Then things, rightfully,
calmed down. Companies started to ask whether they had enough quality data to
really require data scientists.
Yet, one of the major myths from that time still remains: if you have data, a data scientist can turn it into something that will benefit your business. It is possible that you do have valuable data that needs to be transformed, but then again, you may be sitting on fool’s gold. What you really need to find out, is whether you actually need a data scientist or not.
If you know what data scientists do, and what they are for,
that is certainly one part of the battle won. It is not unusual for companies
to hire a data scientist without knowing what that data scientist will do. This
can be down to job spec confusion or it can be down to FOMO or it can be down
to leaderless data projects. There can be an over-reliance on data scientists
coming in to solve all of the company’s data problems. This isn’t technically
within their natural remit. If these things are happening, it does not necessarily
mean you do not need a data scientists. It may mean that you need more
structure, a data team or an experience data recruiter.
Going back to the point of data scientists being expected to
solve all data problems, this also applies to them having to make a low amount
of data count for something more. They also cannot make low quality
automatically become high quality by refining it. This does not mean you should
automatically give up on data projects, but more analysis over where the data
is coming from, how relevant the data being collected is and how much work it
needs to bring it up to scratch, make your data make more sense. Perhaps you
require a consultant in these stages before bringing in the data scientist.
At this point you should really be asking yourself if you
truly intend to commit to a data-driven future for your business. It is worth
considering a first stage of assessment in order to ascertain whether you
require data, possess the kind of data that can be useful and whether you will
give the project the resources, in terms of time and money, that it requires.
The C-Suite will need to be on-board with these developments or there will be
no hope for the projects to get off the ground, strangled as they will be by
lack of enthusiasm and resources. Someone high up within the business should be
evangelical about data, seeing its worth.
Figuring out what you need the data to do is key. Perhaps
you don’t need automated solutions but instead you need manual ones, at least
at first. Defining it before the data scientist has been hired is a wise move
as then they will not spend all of their time defining it, making the C-Suite
wonder why they are bothering. It also means that, when you come to writing the
job spec, you are aware of what problems there are to solve and who you need to
solve them. So many firms send these out before anything is defined and it
wastes time and money. It is even worse when generalist recruiters are tasked
to find the data scientists, as they will not be able to interrogate the job
spec and ask the right questions.
Instead of rushing into data scientist hiring, you have to ask yourself a few questions around who you want, what you want, why you want it and whether you really want it. Once you have figured this out, then comes the step of actually hiring the data scientist.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
