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Love them or loathe them, psychometric tests are now used to assess the suitability of potential employees widely in the business world and by over 75% of the Times Top 100 companies in the UK. So if you haven't already undertaken one, the chances are high that you will at some point. Knowing how to succeed in psychometric testing is your ticket to progressing in your Data or Analytics interview process.
When it comes to psychometric tests there really is a vast array available with at least 5000 aptitude and ability tests currently on the market and every year new ones are devised and added to this. Every company needs to differentiate theirs and this has produced a bewildering range of test names and acronyms.
Tests range from the more standard Personality tests through to specific Aptitude or Ability assessments, which are designed for different skill sets, including verbal ability, numerical aptitude and abstract reasoning. Some companies use a combination but all are designed to identify an individual’s aptitude, personality or ability aligned to a particular role. These tests have been established over many years and are often used with specific groups defined by educational level or job type.
Whatever type of test you experience, the majority are taken online and are generally included during the early stages of selection, as part of either preliminary screening or at the initial interview stage of the recruitment process.
The way that you are likely to perform in a job depends very much on your personality. A personality test is often used in conjunction with interviews to provide a useful insight into your personal style, personality type and how you see yourself. The results of these tests are derived from the answers to a series of multiple choice questions.
There are no right and wrong answers – this test is designed to find out how your behavior is applied to different workplace scenarios. They will ask for information about you, for example do you prefer working in a team or as an individual. You will be required to say true or false, or they may use a rating scale with 1 being what you are most like through to 5 for what you are least like. You should answer each question keeping your focus on which is most/least like you in a work context. Be prepared for anything from 50 to a much more detailed 300 questions.
The great temptation with these tests is to give the answer that you think they want, rather than the true answer, but this really does defeat the point of the test. Additionally, the more complex versions of these tests often ask similar questions in a variety of ways, looking for a trend. If you give conflicting answers to two similar questions it will look as though you have not been answering truthfully. Most people will find that their true answers will match well with what the employer is looking for, but if not then it probably means that it is not the job for you!
In direct contrast, these tests are designed to assess your logical reasoning or cognitive ability and results provide a more objective measure of your potential. They consist of a number of multiple choice questions and can be classified as speed or power tests and will be strictly timed. Speed tests, consisting of questions which are relatively straightforward, focus on how many you can answer correctly in an allotted time. A power test will present a smaller number of more complex questions and is favored when recruiting for professional or managerial level roles.
Aptitude and ability tests can include a combination of:
These tests usually consist of 30-40 questions which need to be completed in 15-20 minutes and involve grammar, verbal analogies and ask you to follow detailed written instructions. They can also include spelling, sentence completion and comprehension. These tests are widely used since most jobs require you to understand and make decisions based on verbal or written information, or to pass this type of information to others.
Verbal reasoning tests are designed to measure your problem solving ability. These questions may take the form of comprehension exercises, which are straightforward (as long as you remember to read the relevant part of the text carefully) or more complex statements where the best tactic is to make notes about what you can deduce from each part of the text. These tests usually consist of 10-15 questions which need to be completed in 20-30 minutes.
Questions could also focus on verbal critical reasoning, designed to assess your ability to use words in a logical way and measure your understanding of vocabulary and the relationship between words. Some questions measure your ability to perceive and understand concepts and ideas expressed verbally.
These tests include a combination from simple addition and subtraction through to more complex data interpretation and numerical critical reasoning, where blocks of information are provided that require manipulation and interpretation. Numerical tests are strictly timed and a typical test might allow 30-40 minutes for 30-40 questions.
Numerical reasoning tests assess your ability to use numbers in a logical and rational way, rather than your educational achievement.
These tests involve looking at diagrams, interpreting the information and understanding underlying patterns in the information. Abstract reasoning tests are thought to give the best indication of your general intelligence and are very widely used.
In this test you are presented with tables and graphs of data, and you must check them against one another. This type of test is used to measure how quickly and accurately errors can be detected in data and is a useful test for roles that deal with large quantities of data that must be read, understood and sorted through accurately.
Depending on the test/s you undertake your results will show a whole range of your characteristics. From what motivates you, your core strengths and limitations to your mental agility and lateral thinking, as well as how well you are matched to the role in question through to how quickly you learn and your ability to hit the ground running in a new job.
Your results will then be assessed in relation to other candidates applying for the role, or candidates who have applied in the past and took the same style test/s.
Psychometric tests aren't about luck; prior preparation will improve your scores and make it easier to focus on what is being sought in the testing process. It’s an old adage that practice makes perfect – but some psychometric tests are not looking for perfect – they are looking to assess your skills, knowledge and attributes against a very specific set of criteria. So the key to giving the best possible answer / score is to be prepared.
Treat this as a positive challenge rather than a potential hurdle in your job hunting and take some practice tests. There are a myriad of practice tests available, so there is absolutely no excuse not to practice and familiarize yourself with the different formats beforehand. And if there are different test ‘levels’ available, practice using the those rated as the highest level of difficulty - that way you will be ready for any level when it comes to taking the real thing! As most of the aptitude style tests are timed, get used to answering a lot of questions within a time limit and learn to balance speed and accuracy.
There are a whole range of companies that specialize in psychometric testing. For practice tests, search for the following companies: (Note that some organizations do charge for you to take practice tests)
For both the practice and actual tests, make sure you have sufficient time to complete them and you can do so in a quiet environment, with no chance of interruptions.
Secondly, it’s better to answer 30 questions correctly/honestly depending on the type of test, than to finish the test but rush so much that you make factual errors or make choices that do not reflect your personality within a work scenario.
And finally, while there are no wrong or right answers in personality tests, there can be indicators of areas in which you would benefit from self-improvement, such as training in ethics or assertiveness. And if a particular aptitude is not up to scratch – consider continuing with relevant practice tests to improve these areas.
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
Visit our News & Blogs portal or check out our recent posts below.
Dream teams from sports to business are an ideal everyone aspires to live up to. But what is it every basketball or football dynasty has which makes them a dream team? What is it that brings individuals together to overcome odds, set examples, find solutions, and create the next best thing? Good management. The need for good management is no different in the Data Science world. Yet according to our latest Salary Guide, poor management is one of the top five reasons Data professionals leave companies. So, let’s take a look at what poor management is, what causes it, and how businesses can better retain Data talent. What’s Your Data Science Strategy? Most businesses know they need a Data team. They may also assume that a Data Scientist who performed well can lead a Data team. But that isn’t necessarily the case. Managers have to know things like P&L statements, how to build a business case, make market assessments, and how to deal with people. And that’s just for a start. The leader of a Data team has a number of other factors to consider as well such as Data Governance, MDM, compliance, legal issues around the use of algorithms, and the list goes on. At the same time, they also need to be managing their team with trust, authenticity, and candor. The list of responsibilities can be daunting and if someone is given too much too soon and without support, it can be a recipe for disaster. Other businesses might believe that a top performing Data Scientist would make a good manager. Yet these are two different fields. Or you might look at it this way. If you are willing to upskill a top performing Data professional and train them in managerial skills, giving them the education and support they need, that is one solution. Another solution is to create a Data Science strategy which brings in people with business backgrounds. Data Science is a diverse field and people come from a number of backgrounds not just Computer Science or Biostatistics, for example. Now that you’ve seen what might cause a manager to fail, let’s take a look at a few tips to help you succeed. Seven Tips for Managing a Data Team Managing a team is about being able to hire, retain, and develop great talent. But if the manager has no management training, well, that’s how things tend to fall apart. Here a few tips to consider to help ensure you and your team work together to become the dream team of your organization: Build trust by caring about your team. Help define their role within the organization. Ensure projects are exciting and that they’re not being asked to do project with vague guidelines or unrealistic timeframes.Be open and candid. Remember, Data Scientists are trained in how to gather, collect, and analyze information. If anyone can see right through a façade, it will be these Data professionals. Have those “tough” conversations throughout every stage of the hiring, onboarding, and day-to-day, so that no one is caught unaware.Offer consistent feedback. And ask for it for yourself as well from your team.Ensure your team understands the business goals behind their projects. Let them in on the bigger picture. Think long-term recruitment for a permanent role, not short-term. If you have an urgent project, consider contracting it out. Prioritize diversity to include academic discipline and professional experience. Does the way this person view the world expand the knowledge of your team’s knowledge? Dream teams don’t always have to agree. Sometimes, the best solutions are found when there are other opinions. Finding the perfect, “Full Stack” Data Scientist or Data Engineer or Analyst is not impossible, and retaining them can be even easier. If you’ve done your job well, your team will trust you, have a balanced skillset, and understand how their work supports the organization and its goals. For more information on how to be a great manager, check out this article from HBR. Ready for the next step? Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
11. July 2019
From the first genome sequencing in the second revolution to Life Science Analytics as a growing field in the fourth industrial revolution, change has been both welcomed and fraught with fear. Everyone worries about robots, Artificial Intelligence, and in some cases even professionals who have stayed current by keeping up-to-date with trends. And it’s beginning to affect not only “office politics” within the tech space, but even interviewer and interviewee relationships. We’ve seen a growing trend of apprehension between Computational Biologists and Machine Learning Engineers. What could be the cause? Aren’t they each working toward a common goal? It seems the answer isn’t quite so cut and dry as we’d like it to be. Here are some thoughts on what could be driving this animosity. But first, a bit of background. So, What’s the Difference? Computational Biology and Machine Learning are two sides of the same coin; one sets the framework and the other applies what’s been learned. Both use statistical and computational methods to construct models from existing databases to create new Data. However, it is within the framework of biomedical problems as computational problems, that there seems to be a bit of a breakdown. It’s one thing to have all the information and all the Data, but its quite another to know how the Data might interact or affect the health and medications of people seeking help. This is the job of those in Life Science Analytics. Determine through Data what needs to be done, quickly, and efficiently, but at the same time, ensure the human element is still active. A few examples of Computational Biology include concentrations, sequences, images and are used in such areas as Algorithmics, Robotics, and Machine Learning. The job of Machine Learning can help to classify spam emails, recognize human speech, and more. Here’s a good place to start if you’d like to take a deeper dive into the differences between the two or read this article about mindsets and misconceptions. Office Politics in the Tech Space Circling back to the concern between Computational Biologists and Data Scientists with a focus on Machine Learning. The latest around the water cooler within the tech space is that those with a biological background who understand Machine Learning are looked upon as dangerous to the status quo. But, as many of our candidates know, it’s important to stay on the cutting edge and if that means, upskilling in Machine Learning so you have both the human element as well as the mathematical, robotic components, then that is more marketable than just having one or the other. The learning curve in biology training within the Life Sciences Analytics space means Computational Biologist with a Machine Learning skillset is best able to apply Data Science and computer science tools to more organic and biological datasets. Someone with just a computer science background may not have the depth of knowledge to understand how these models, systems, and data affect and impact medicine. Computational Biologists who are trained simultaneously in computer science and biology, and are a little heavier on the biology side, see Machine Learning Engineers as a threat because utilizing Machine Learning and other cutting-edge tools could mean their job is on the line. They worry their job will fall by the wayside. That when somebody proves Machine Learning is faster and more efficient the impetus might be why hire a Computational Biologist when a Machine Learning engineer will do? It’s like when a lot of people joke about how robots are going to take over the world and everybody will be out of a job. I think the worry with some folks on the Computational Biology side is that maybe they just aren’t up to date with their training or haven’t kept up with cutting edge of technology. With a Recruiter’s Eye While what I’ve seen agrees that, yes, Machine Learning is incredibly helpful and fast and you can get through so much more data. But its still that understanding of biology and chemistry that you will need because you need to be able to understand, for example, how these proteins are going to be reacting with one another or you need to understand how DNA and R&A work, how best to analyze, and what analyzing those things means. On the other hand, just because you know, “oh, this reaction comes out of it”, if you don’t know why that is or how that could impact a drug or a person, then you don’t really have anything to go on. There’s a caveat there. Though there may be concerns among Computational Biologists and Machine Learning Engineers, at both the upper and entry levels, it’s still the technical lead who will say, “we really do need somebody with a biological background because if we get all this Data and don’t really know what to do with it, then we’ll need to hire a Project Manager to converse between the two and that’s an inefficient use of time and resources”. What I hear most often is a company wants a Computational Biologist but they also want someone who knows Machine Learning. But they don’t want to compromise on either because they don’t understand there are limitations to things. We all want the unicorn employee, but we can’t make them fit into a box with too specific parameters. It’s a Fact of Life Any job, whether it’s in the tech industry, the food industry, Ad Optimization, or even recruitment, uses Machine Learning in one way or another. Yet compared to spaces which work on sequencing the human genome, it's amazing to see how far things have come. It used to take days to process DNA. Now you can spit in a tube and send it off to 23andMe to learn a little about your health. That’s what Machine Learning enables people to do. But it doesn’t mean Computational Biologists are going to fall by the wayside. It means there will be times you’ll have to liaise more between the two groups. It means you’ll be more marketable by adding Machine Learning to the work you’re already doing or taking some classes in Computational Science, for example, to keep your skills up to date. It’s a Transparency Issue Ultimately, it seems the heart of this apprehension comes down to a transparency issue. For example, let’s say companies begin to bring in AI people and suddenly the staff already in place begins to get worried about the security of their jobs. Even in an industry tense with skills gaps, the fear still abounds. In coming back to speak with the Hiring Manager, it became clear the animosity is even more prevalent than first imagined. So, it’s important to get input from within the company and develop a unified story, a unified message across departments, and especially within the Life Science Analytics and Data Science teams as well. In other words, “keep people in the loop.” If it’s happening to this company, it seems other companies may be facing this same issue. However, it’s not going away and is creating a kind of competition between the old guard and the up-and-coming startups. For example, any new company is going to want to integrate AI and will be asking the question how best to integrate it into their structure. They might also ask how best to optimize the ads coming through AI. This is just another way of how companies are catching up, but also how people are catching up to the companies. Technology is coming whether you like it or not. So, if you want to stay marketable and work on really interesting projects, there’s always going to be the challenge of staying up-to-date and different companies attack this in different ways. Stay open minded, keep an eye and an ear out for ways to stay on top of your game. Even just taking a few minutes to watch a YouTube video, listen to a TedTalk or a podcast, so you can talk about it and be informed. These are some really simple ways to stay on the cutting edge and help you figure out where you can grow and improve for better opportunities. Ready for the next step? Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
04. July 2019
We’re halfway through the year and our salary guide is out. If you wonder where you are, where you’re going, and if you’re a business, how you’re going to get there. Well, then, you’ve come to the right place. Articles have touted Data Scientists as the “rock stars” of the 21st century, but even rock stars need their managers and roadies. Who else will build the stage and plan the tour? And in the world of data, it all begins with the Data Engineer, laying the groundwork, the foundation, and the framework. These are the stars behind the scenes who make it possible for Data and Data Scientists to be front and center. Send a Data Engineer Over As prevalent as Data has become in our lives and as its importance grows, there remains the challenge of Data Management. If you don’t know why something is built or how to navigate the structure, the Data you do receive may not make much sense. Your guide in this journey is the Data Engineer, one of the most important pieces of your Data Management puzzle. These highly skilled and sought-after professionals should not be confused with a Software Engineer, though some elements may be transferable between the two. The building blocks to put massive amounts of Data into a scalable system both reliable and secure takes a unique set of skills. Humans at the Helm as Skills Shift As much as we depend on Data today to help determine actionable insights for our business and as much as we hear about the rise of machines in the form of Artificial Intelligence, Machine Learning, and Deep Learning, it is ultimately humans who are at the helm. It is the people behind the curtain of Data who will build it, run it, and make it work. It is also people who are typically the biggest costs in a project. Finding the balance and ideal candidate, the right person with the right skills for the job, is critical to success. And if you’re starting from the ground up, Data Engineers who can work with the core tools of databases and Spark, for example, will see their opportunities grow. In our Salary Guide for 2019, we learned one of the skills most sought after by companies today is knowing AWS/Azure and moving Data Lakes into the Cloud. Small businesses and startups are moving to the Cloud to help them scale their Data, but someone still needs to lay the groundwork, whether it’s for the small business or the public cloud providers. Data Engineers are in high demand and it doesn’t look as though things will be slowing down anytime soon. The field is slick with potential. The Time Has Come for Transparency Data is binary gold and, with enough of it, you can read or estimate the mind of your customer or you can wreak havoc on someone’s life. Just a year ago, the European Union put into place rules and regulations as well as financial consequences for poor Data Governance under the General Data and Protections Regulation Act (GDPR). Though the U.S. doesn’t yet have a similar law, there are still plenty of mandates to be aware of by states, unions, and countries. One Final Thought As roles and technology evolves, it’s important for businesses, employees, and stakeholders to evolve as well. Whether that means making sure to implement practices for Data transparency or upskilling and reskilling your workforce to keep up or simply knowing the trends of forward-thinking companies to scale your own business. Data fuels digital innovation and organizations who are prepared to find solutions will benefit. Want to know what else is trending in big data? Here are a few trends in Big Data forward-looking organizations should look out for in this year and toward the next. Are you a business who knows you’re ready to scale up and hire a Data professional? We have a strong candidate pool and may have just the person you need to fill your role. Are you a candidate looking for a role in Big Data & Analytics? We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
20. June 2019
From startup and small business to large enterprises, each type of business requires a unique blend of Data professional. Though in today’s world, much of the Data being gathered, catalogued, and analyzed happens both in the Cloud and on a hard drive, each type of business has a different need, budget, goals, and objectives. But there is one thing each and every business will have in common. At the heart of the Data team will be a Data Engineer. The Three Main Roles of a Data Engineer This is an analytics role in high demand. It is a growing and lucrative field with steps and stages for nearly every level of business and education experience. For example, a Data Scientist interested in stepping into a Data Engineer role might begin as a Generalist. In all, there are three main roles for each level and type of business – Generalist, Pipeline-Centric, and Data-Centric. Let’s take a quick look at each of the roles with an eye toward the type of person who might be the best fit: Generalist – Most often found on a small team, this type of Data Engineer is most likely the only Data-focused person in the company. They may have to do everything from build the system to analyze it, and while it carries its own unique set of skills, it doesn’t require heavy architecture knowledge as smaller companies may not yet be focusing on scale. In a nutshell, this might be a good entry point for a Data Scientist interested in upskilling and reskilling themselves to transition into a Data Engineering role.Pipeline-centric – This focus requires more in-depth knowledge working with more complex Data science needs. This type of role is found more often in mid-sized companies as they grow and incorporate a team of Data professionals to help analyze and offer actionable insight for the business. In a nutshell, this role creates a useful format for analysts to gather, collect, and analyze each bit of Data at each stage of development.Database-centric – This role is found most often in larger companies and deals not only with Data warehouses, but is focused on setting up analytics databases. Though there are some elements of the pipeline, this is more fine-tuned. In a nutshell, this role deals with many analysts across a wide distribution of databases. A Fine Balance Between Technical Skills, Soft Skills, and Business Acumen While it’s important for anyone filing this role to have deep knowledge of database design as well as a variety of programming languages, its equally important to understand company objectives. In other words, once the groundwork is laid and the datasets established, it’ll be important to explain what it is the business executives need to know to make the best decisions for their business. Knowing how and what to communicate to executives, stakeholders, and your Data team also means understanding how to best retrieve and optimize the information for reporting. Depending on your organization’s size, you may need both a Data Analyst or Scientist and a Data Engineer. Though this is less likely in medium and larger enterprises. On the flip side, in order to understand the business’ needs, you’ll also need to be good at creating reliable pipelines, architecting systems and Data stores, and collaborating with your Data Science team to build the right solutions. Each of these skills are meant to help you understand concepts to build real-world systems no matter the size of your business. One Final Thought… Do you like to build things? Tweak systems? Take things apart and see how they work, then put them back together better and more efficient than before? Then Data Engineering might be for you. Are you a business who knows you’re ready to scale up and hire a Data professional? We have a strong candidate pool and may have just the person you need to fill your role. Are you a candidate looking for a role in big Data and analytics? We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to firstname.lastname@example.org.
11. June 2019