For job candidates, time can feel like it is ebbing away
As a hiring manager, does it feel
like you never stop interviewing? As a candidate, does it feel like
interviewing never ends? What is the normal amount of candidates to take
through to interview stage and how many interviews does it take to know which
candidate is the right one?
Those in recruiting circles
rarely talk about these topics as they are considered a standard part of the
business of recruitment. Typically, 6-10 people are brought through to
interview and those who are, should expect to face 2-3 rounds of interview but
it can lead to 5 or more.
If can be disheartening when, as
a candidate, you find out about another round of interviews, after you aced the
first and second. It is even worse when, after jumping through all of those
hoops, it all ends in rejection, especially when you felt that you dazzled them
and answered every question.
The major confusion comes when a
candidate imagines the path to a job. Their imagined path will probably contain
seven steps from searching for the job vacancy to sending resume and cover
letter to the invite to the interview to accepting the offer to starting work.
In reality, there are many more steps in the process and when facing ten or
more steps, the candidate can start to lose hope. This is amplified
exponentially if the process goes on for over a month.
Thanks to a glut of CVs being
sent in for jobs on online recruiting platforms and an explosion in the number
or generalist recruiters, there now requires more of a ‘whittling down’ of the
prospects. So, weirdly, it is no walk in the park for the hiring manager either.
Recently, with the Covid-19
crisis, more managers have been exposed to video interviewing, so now, in
addition to the first phone interview, there are now multiple rounds of video
interviews, made easier by services like Zoom and Microsoft Teams. In the past,
there were people who were difficult to wrangle into the same location, but now
they can join from anywhere with an internet connection. It feels though, that
because the interviews are not face-to-face, that sometimes hiring managers
might feel more hesitant to say yes to a candidate, which leads to more rounds,
just to make sure. In large firms, the decision may not just be up to one
individual so the hiring pipeline is blocked further.
Due to the internet, and the ease
in which people can apply for jobs or can be headhunted, the prospects can be
snapped up for other roles before HR can say ‘second interview’, which throws
more uncertainty into the mix.
Some hiring managers are now
moving away from the rigidity of the 6-10 rule and are arranging to interview
candidates they are interested in as soon as they see a resume that catches
their eye. This is down to both the fact that video interviews make things
easier to arrange, and also, due to the fact that they are desperate to fill
positions, especially when it comes to Cloud, Data and AI. They will then
likely park the candidate they like until other candidates crop up, then they
compare. This can be excruciating for the candidate who genuinely made a great
impression. The problem is that they may fade in the memory of the interviewer
over that time.
With an average of 250 applicants
for every job, managers need to thin the herd. First they either look through,
or use software to sift through, the CVs. Then come the phone interviews to
thin it even further, with managers looking for tell-tale signs in a
not-so-formal setting. Perhaps the candidate trips up over what is on their
resume. If you are in the top 2-3% who actually make it through to the next interview,
that means you have a good chance of getting the job.
Other factors can affect whether
you actually get that job, of course, including cultural fit, primary traits
the managers think will work within the role, salary etc.
To answer the question whether companies are wasting candidates’ time, there is no answer other than how the candidate feels. There are reasons behind the long-drawn-out process, yet some recruiters are changing with the old ways and the future is looking brighter for both candidates and hiring managers.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
Managers struggling to find data hires must look to alterative strategies
Managers can have a hard time
when it comes to finding data scientists or AI guys to fill their teams. Sure,
on the surface, it looks like any other candidate search but there are a few
things that generally hold back their company’s progress in the data field and
sometimes it can take a while before the mistakes are spotted. Companies have
never spent as much money hiring people as they do today, and so much of that
is wasted because the standard way of doing things is so out of date.
One of the recurring problems,
when a manager is trying to fill a data position, is that they do not know what
they are looking for to begin with. This can lead to confusion, with job specs
changing during the process or over an extended period with round after round of
attempted hirings still resulting in unsuitable candidates and a lot of wasted
time and money.
This is extremely profligate and
occurs with great frequency in companies that recruit and hire in-house.
Managers are adrift, having to fend for themselves, working out for themselves,
and on their own, what they need and how the job ads should be worded. Software
is then used to sort resumes by keywords. The company may have also purchased a
license for a new piece of software that promises to be the magic bullet for
hiring.
Despite what is promised, there
is no shortcut to strong results when it comes to recruitment. Trying to build a
data team is difficult at the best of times, let alone when requirements aren’t
locked down. A constantly shifting need only makes the job even harder to do
and without an expert there to step in and correct the missteps, the missteps
will only continue.
Shifting job specs are a real
problem in relation to data jobs. It can be a huge change, like a switch from a
data scientist to a big data developer, or a more understandable error, like
changing from a search for ‘a clicker’ to a ‘coder’ data scientist. Whatever
the necessary position and whatever the advertised position, one thing is
clear: the confusion is draining resources.
Recruiters can offer the solution
but not all recruiters are made equal. There is a stark gap between a recruiter
who has partnered with a company and is offering a consultative service, and a generalist
recruiter working on commission. The recruiters who are working on commission
alone are a law unto themselves. Even if you fail to factor in the fact that
recruitment has pretty much no regulation, there can be some cut-throat
operators out there.
Let’s say that you require a data
scientist. You set a task to a recruiter to brings in the candidate and they
come back with a great lead, perfect for what you are looking for, and you are
willing to pay the fee. The only problem is that another company is also
looking for that same data scientist and their fee is higher. Because the
recruiter has no loyalty to your company, the generalist recruiter will take
the higher offer. If this isn’t dealt with, it will happen over and over, and
your data project will fall further and further behind. Sharing prospects is
not illegal and, without exclusivity, you cannot blame a recruiter for taking
more money. As a manager, you must know that there are other options open to
you.
Another aspect of recruitment
that is rarely acknowledged is the fact that, without a partnership agreement, generalist
recruiters can just give up. When the task becomes too hard or a few possible
prospects have been rejected, a recruiter can just stop working for you and
move on to an easier task for someone else.
These are not the only problems facing harassed managers trying to fill data positions, but they are the major ones, and they are all avoidable. If you don’t have the knowledge, find someone with the knowledge. If you are dissatisfied with the results you are getting from the recruiters in which you have placed your trust, build a relationship. The results will surprise you.
Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.
The correct fit will create value for your company
No one goes into data science
unless they are curious about the future and intrigued by tech innovation. With
more and more firms attempting to harness the power of their data, they need
great data scientists to make their plans a reality, but how can your business
attract the best?
Impact
Solving difficult problems is one
of the major draws. Data scientists are like anyone else in that they like to
see that the hard work they put in is paying off in results for the company
they are working for. A lot of businesses struggle to utilise data in the way
it should be used and often worry about both the balance sheet and worry that the
way things have been traditionally done will be disrupted. Both of these issues
can negatively impact a data professional’s sense of worth in a company. If
there have been derailed projects in the past, the most important thing to do
is to admit that your firm has learned from its mistakes and reassure your
candidates that their work will be valued this time. Achievements must always
be acknowledged.
Innovate
A feeling of being underutilised
can also be negated by allowing your data scientists to work on their own
projects and inventions, as long as it does not impact their regular work. The
idea that data scientists are nerds who love nothing more than to just sit
looking at numbers for hours on end, must end. As mentioned before, they are
curious about the future and tech innovation. A data scientist, choosing
between a standard post and one where they are aware that the company
understands that they are a human being who will become bored doing the same
tasks over and over, will choose the intelligent option every time. The best
firms know this and it pays off for them.
Improve
As your data scientists have a
great overview of your business from the data perspective, they can see things
that perhaps other departments cannot. Allow them to suggest improvements and
show them that you value their input. This can be a great motivator. Another
way they can help improve your business is to work cross-departmentally,
perhaps with sales, customer relations or ops, so that they can see where the
work is impacting and understand any problems or needs they should be solving.
The learning can be a two-way street too, where other departments, that can sometimes
be sceptical of data, can be reassured that data science isn’t nonsense or
voodoo. That kind of cohesive teamwork is really attractive to prospective
candidates who might have experienced the opposite.
Importance
Separating the data team from the
C-Suite is alienating on both sides. Executives will undervalue the data
scientists and the data scientists will not be contributing their knowledge and
can be left behind when it comes to understanding the future mission of the
business. They may be working on the wrong problems or they may know their current
work is futile but are not being prioritised or listened to. There must be a
two-way conversation. Data professionals must be encouraged to communicate
their data in a narrative that the C-Suite will understand, and C-Suite must
ask for solutions from the data professionals when building their plans.
These are only a few examples. A company should also understand that a data scientist’s needs may vary from, example, a sales team member. Things like flexibility in where and when they work, ensuring that they have all the tools they require to do the job properly and offering additional training for them to keep up-to-date with new tech or packages, all contribute to making a business attractive to the best data scientist.
Curious about how Zenshin Talent can help your organisation hire the best candidates? Contact us today for a no-strings conversation about your needs and our experience.
Hiring the wrong type of Data Scientist can take you down the wrong avenue
Whilst the sector can be complicated and bewildering, one of
the main problems facing businesses is the fact that they are often hiring the
wrong data scientist for their needs. As crazy as it may seem, there is deep
confusion within firms that require data professionals but what can be done?
The blame does not just rest on the shoulders of those who
are hiring. Prospective candidates may be averse to highlighting the mistakes
made by employers if it means that they do not get the job.
Some who might be referred to as data scientists, are closer
to statisticians and data analysts. They may work with Excel and they may
create business insight visualisations. They may be nicknamed ‘clickers’ and
they have a positive role to play in businesses, but they are not coders and
cannot make or replicate the breakthroughs that companies desperately need
during these times but may be earning the salary of the more qualified coding
data scientist due to the confusion.
The Senior Economist for Glassdoor, Daniel Zhao, states, “This
muddling of job titles is changing the composition of the data scientist
workforce and holding down wages.”
This is having an effect in two ways. ‘Clicker’ data
scientists may be receiving higher salaries meant for ‘coder’ data scientists,
or they may be receiving the normal salary for someone in that role. This gives
the employers the false impression that data scientists fit into this wage
bracket and any data scientists with higher expectations are being unrealistic
and thus, will never be hired.
This all starts at the job spec stage. A lot of these
descriptions do not really specify what the role requires. This can turn away
seasoned data professionals and leave the door open for those who are less
skilled. Companies accepting candidates who have 50% of the requirements can be
hiring the wrong data scientist. You must not be afraid to hire someone who has
100% of the requirements due to suspicion or fear of over-qualification. There
are many facets of this problem because businesses can, conversely, scare away
good prospects by demanding too many years’ experience in a certain field or
too many qualifications that are not relevant. Perhaps the company has decided
to cover all bases by advertising the role as a senior role but research has
shown that this scares away perfectly-suited potential candidates. Specificity
is the watchword here, as it will guarantee that only the right scientists are
interviewed and hired, and it also means that collectively, data as a whole
will move forward into a bright future.
Data wrangling and cleaning big datasets require programming
and coding skills. This is the meat and potatoes of the data scientist and it
cannot be done by someone lacking coding skills. Point-and-click has its uses
but that software does not have the complexity to do the heavy work. Data
scientists need to be able to code in languages like Python and R.
This kind of mix-up was bound to happen when you have companies launching data projects for the first time or seeking to exploit their data without a full understanding of how these goals will be achieved. Each discipline uses the same or similar terms so it is understandable that management who are just learning the terms would assume that these roles are the same. This naivety can be countered by employing or partnering with experts who can recognise higher skills, who know how to build data teams and what the structure of those teams should be. This isn’t just a problem that can we waved away as ‘geek stuff’. Your company’s bad data analysis could lead to lawsuits and heavy fines, so it is worth bearing in mind that nipping this problem in the bud before it gets out of hand will pay off well in the future.
Curious about how Zenshin Talent can help your organisation hire the best candidates? Contact us today for a no-strings conversation about your needs and our experience.
At the root of Data Science is
the need to use data in order to ascertain certain information and answer
certain questions. Businesses that either possess data or are looking to
collect data, need data scientists and those data scientists need a few different
skills up their sleeves. We have put together a list of the top skills to look
out for in the data scientists you are hiring in 2021.
Data Visualisation and Wrangling
If a company has mined or
acquired data, which is not readily useable, it must be made useable. Raw data
must be wrangled into a form that can then be understood, processed and
modelled. This can be done by combining from multiple sources or cleansing the
data of its mistakes and impurities. Once these actions are performed, it
allows data professionals to analyse the data, confident that it is accurate. Figuring
out what the data is telling them is another challenge that data scientists
routinely face and visualisation helps two-fold. Firstly, as a means of them
learning from the data and understanding what it has to tell them. Secondly, it
helps them craft a narrative around, say, consumer behaviour, using visual
graphics (with charts, maps, etc.) to communicate and convince the management.
Stats and Probabilities
The use of mathematic systems to
decipher and decide what data to use and how to use it, is an important factor
and your prospective candidate will need to be a maths whizz. This doesn’t just
count for known numbers, but they must also be able to adequately estimate
unknowns from knowns too. Statistics are usually based on probabilities so the
two go hand in hand. Trends and dependencies can be identified using stats and
probabilities which can aid in future planning for the business via data
modelling. Patterns can be identified and problems can be predicted.
Cloud
An understanding of Cloud and
Cloud Computing is incredibly important to data scientists in 2021, as it means
they are able to access databases and tools on platforms from Google, IBM,
Windows and Amazon. Access to huge amounts of data resources helps the data
scientist mine, acquire, wrangle and analyse data much quicker than via
traditional methods. Modelling and optimising performance are also made easier
via the cloud, and savings in time, mean savings for your business.
Calculus
Multivariate calculus is an
important skill within machine learning due to the use of unknowns and
predictors. It is used in such areas as gradients or plotting and values for
functions such as sigmoid, step, vector or cost. Matrix algebra and neural networks
also come into play.
Software and Programming
Python is usually the major
language you find data scientists working with, but of course there are many to
choose from, including Java, Scala and SQL. Python is usually chosen as it is
kind of a ‘one-size-fits-all’ contender. It will come as no surprise that
programming is important within data as it covers a lot of skills which are
integral to create useable data and deal with it.
Database
Yes of course that perfect
candidate will have a lot of skills and one of the most important is the
ability to define, store and index that data for ease of retrieval and use.
Expertise within requesting and file structure will help your candidate cut
through to what they need in order to test and manipulate it.
Curious about how Zenshin Talent can help your organisation hire the best candidates? Contact us today for a no-strings conversation about your needs and our experience.
