Over-interviewing Is Killing Your Business

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.

The Dangers Of Recruiting With AI And Machine Learning

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.

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.

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.

Who We Are

Working Toward Your Future

Our mission is the empowerment of tech companies or businesses running tech projects to create world-class teams and reach new tech frontiers. We believe in a data-driven future and know that the best humans with the requisite skillsets are required in order to get us there.

We know our specialisms inside out. Our specialisms are:

AI

Data Science

Machine Learning

Robotics

Computer Vision

Natural Language

Founded on the principle that for AI and Data to progress as it should, the recruitment for these teams must never be left to generic recruitment companies who do not specialise within those disciplines. Since we started up a few years ago, we have placed data scientists and AI experts into companies, we have created perfectly-balanced teams from scratch, we have been consulted on the best course of action for companies just starting out on data projects and we are growing far beyond our business plan.

Zenshin Talent is adept at finding those candidates who are perfect but who are not even actively looking for their next role. Thanks to our experience, we avoid the pitfalls that generic recruitment agencies

When we become a recruitment partner, that means that we are invested in the success of the team and we will spend all of our time and resources ensuring that the best and brightest hires are selected. Generic recruiters can sometimes become desperate to find candidates to put forward but Zenshin Talent can build a rapport with prospects because we know what we are talking about and we know exactly what our partners are looking for.

The core beliefs of Zenshin Talent are:

Integrity – strong morals lead to a strong future

Boldness – to make a difference, you must stand apart from your peers

Honesty – only when we speak the truth do we know the facts

Trust – relationships must be built on trust or they fall apart

Accountability – responsibility and respect go hand in hand

Innovation – there is no success or growth without innovation

Teamwork – teamwork makes the dream work

These are principles that Zenshin will never compromise.

If there is a larger goal or purpose that Zenshin Talent wants to achieve, it is for Data, AI and all of our other specialisms to receive the respect they deserve. We know that companies may not understand fully what these disciplines mean for the future but we want to play our part in demonstrating and teaching those companies just how important they are, not just for the business, but for the world. These candidates have honed their skills and we want to see them get the respect they deserve too.

Zenshin Talent is not a stuffy and old-fashioned recruiter. It simply cannot afford to be in this fast-moving world. We know the job we need to do, we achieve it, we look to innovate when it is needed and we also retain that human edge, something businesses within tech sometimes lose. To us, it starts and ends with a human because that is how you build human connections. partners know this and you will too. Productive working relationships are paramount.

The beliefs we share with our partners mean that respect and openness are key. We promise the candidates that they will be valued within our partner companies because we make it clear to the partner why the project is so important. Everyone must be on the same page and we make sure that they are. Our passions and affinities are shared and that results in a great partnership.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.

Why Is There A Huge Failure Within Data Recruitment?

A 30% failure is huge within any context

It is becoming apparent that many companies are failing to select the correct candidates to hire and it is getting to a stage where those involved must be made aware in order to rectify the mistakes.

A startling figure is that 70% of hires make the first year anniversary at their respective companies. That may seem impressive but one must bear in mind that that means that 30% of new hires don’t even make it to a year working in data at the company.

In what other area of the business would that percentage of failure be acceptable? None. If you failed 30% of the time in any other area of the business, it would be a catastrophe, so why is it okay in data recruitment?

I mean, of course this may be happening in other areas. Only recently we published a very popular blogpost about how only 20% of C-Suite hires are the right fit for their company. We are focusing on data recruitment as it one of our areas of expertise.

Usually companies have adapted their style of hiring for data scientists from their style of hiring for software developers, especially larger companies. This often involves tests such as brainteasers involving coding or statistics, which candidates actually practice for and which, in reality, are a waste of time for everyone involved. These clean-cut problems ignore the fact that data professionals can be working with messy data. While using guidelines from software developers is a step in the right direction from generic or vague hiring practices, it is obviously not going far enough to highlight the best candidates and their skills. If a company is working from an old model, how can it expect to attract cutting-edge candidates.

Outdated techniques are just one side of the problem. Finding prospects with the right skills is of utmost importance but how many businesses know what they are looking for in this area? Each candidate must have a mixture of skills involving business, databases and maths & stats. They should be adept at model building and should be used to using different models to solve various problems.

They must have experience in Machine Learning but if they do not, sometimes their degree may have involved modelling and they just need to brush up on the skills in order to become a valuable member of the team. Team members must be able to, firstly, find the data, and secondly, process the data. These are two separate skills and the candidate must have both. The fact that this is counterintuitive to those who are not experienced in the sector means that hiring managers can be tripped up by this and other similar eccentricities with data. And we have not even touched on the different coding languages a prospect may need to know. For example, data analysts often only work in Excel which means they are ill-suited to other data roles. It is complex and those with some or no experience within it can be challenged by it.

On top of all that, a data expert should be able to understand problems within businesses, recognise how data can be used to solve it, solve it and then translate it back for the layperson to understand. If one part of this solution is incorrect, the problem remains unsolved. They should also be able to handle pressure, learn new things and mix well with other team members.

It is clear now why so many data professionals do not make it to their anniversary in their company and a lot of the time it is not down to flaws within the candidate. Companies should take care when hiring to ensure they are hiring the right fit for their data team, prospective candidates should be encouraged to create their own portfolio to showcase their skills and someone who has expertise in evaluating these should be in charge of progressing those prospective hires to the next stage of recruitment. With more awareness, together we can get those anniversaries up to 100%.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.

Why Are So Many Businesses ‘Cold Starting’?

Many companies are cold starting their data projects

As mentioned in the previous blogpost, Data Professionals are looking at the door when it comes to their current roles due to a few factors, a major one being companies being unprepared and the new hire having to start things from scratch. This situation is often referred to as ‘Cold Starting’ but why is this happening?

When starting to build a team for projects within data, the first line of priorities are collection including user generated content, external data, instrumentation and logging. Useful, useable data is key for taking advantage of AI. Starting with an assessment of whether the data being collected is the correct type of data to satisfy business needs further down the line. Obviously, this varies and depends on the type of business you run, so whether this is categorical or numerical, this should be in place before you expect there to be any momentum when this data can be assessed.

Some companies fail to understand that these foundational blocks must be in place first and that there can be trial and error before this is all up to speed. What we mean by this is that firstly the requisite types of data must be sought out and then interrogated in order to discover whether the data being pulled through is actually correct. Within payment transactions, for example, there are different types and if the incorrect type is being collated, this can have far-reaching repercussions.

This initial stage, as well as ensuring that the right data is being used, requires resources. This means a leap of faith on the part of the company which, in many cases, does not yet comprehend the true value of what is being proposed. This is what we believe is behind the cold starting. Companies adopting this new approach to their information are unsure, until they are sure, and this results in a rush to get all parts of the machine in place and moving at the same time. Misunderstanding the complexity can result in false starts, a high turnover of staff and the aforementioned failures of data.

From that point onwards, data should be analysed. Analytics means the systematic analysis of stats or data and this can involve many different KPIs depending on a business’s needs. AI should then use algorithms to improve KPis and these improvements will feed back into the system ensuring optimisation and higher profits for the company. Of course, we have simplified it here and there are more moving parts in reality but it is surprising how many people don’t cover the basics when starting.

The company will need someone with the appropriate experience on hand to guide the whole process and ensure that the correct seniority of staff are hired and the staffing levels are correct during all stages of the projects. Another problem that can arise if this does not happen is when a junior data scientist is tasked with creating the system and they use algorithms from practice sessions on real world business issues. Getting the right mix is so important.

Indecision can result from a clash of priorities at C-level between wanting the best and wanting the cheapest solution to the problem. If one or more members are afraid that AI is just the newest fad, which will fade some time soon, then that explains why they hold off on it until they can hold off no longer. This is very true of those industries that are not traditionally data-driven.

Start-ups have an easier time of it because they are often engaged with data during the planning process. Older businesses need to reimagine their business from the bottom up and sometimes that can be a daunting possibility. The truth is that the future won’t wait for a business so the sooner they can get onboard with AI and Data, the better. As the old saying goes, “The best time to start something is years ago. The second best time is now.”

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.