Are Companies Wasting Their Job Candidates’ Time?

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.

Why Do Managers Make The Same Mistakes With Data Hires?

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.

How Do Companies Attract And Keep The Best Data Scientists?

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?


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.


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.


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.


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 Data Scientist Will Damage Your Business

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.

Top Skills Your New Data Scientist Needs

The world in 2021 needs skilled Data Scientists

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.


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.


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.


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.