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How To Effectively Prepare For An Interview

If you’re preparing for an interview, video or otherwise, it’s important to think beyond Googling ‘common interview questions’ and scanning the company’s website. It’s important to make a great first impression and by preparing properly, you’re in a better position to achieve this. Plus, you’ll feel more confident and will be able to give more convincing answers that will help prove why you’re the perfect fit for the role. With that in mind, here are our recommendations for how to best prepare for an interview: Find out what type of interview it is Even if a company isn’t working entirely remotely, it’s likely you’ll face a variety of interviews throughout a their application process. Most processes last between two and three stages of interviews, any many vary in how these are conducted. They could be: Face to Face – Expect these to last between 45 minutes and 2 hours. The questions will be likely be strengths- or competency-based. Phone – This is often used early in the process, we’d expect these to last approximately 30 minutes and are designed to get a feel for your skillset and experience. Video – These are becoming increasingly popular with employers, and can be live or pre-recorded. They tend to last around 30 minutes. Assessment Centres – You’ll attend these with other candidates and take part in a variety of tasks presentations, team exercises and psychometric tests lasting a full day. Get to know the company  Don’t just look at their About Us page. Read about them, their clients and their products or services. This will help you learn about what they do but also learn how they see themselves as business and what they feel makes them different from their competitors. This will help your interviewer understand you ‘get’ them and understand their business. Research the team  As well as getting to know the company, we’d recommend taking a look at your interviewer’s LinkedIn profile and seeing what they’ve posted and where they’ve come from. Also, take a look at the “Meet the team” pages on the website to gain an insight into who you may be meeting throughout your process. Glassdoor is a good place to go for company reviews but take them as a guide not fact as they’re anonymous reviews by current or former employees.  Prepare your own questions It’s likely your interviewer will ask what questions you have for them. This is a great opportunity for you to get the information you need to figure out if this is a job you really want and can see yourself doing. Think about what you really want to know about the position and the company. Things to think about could be: What are the biggest challenges in this position? What would be the expectations of me 3/6/12 months in? Could you describe what a typical day is like in this position? Not only does this help you build a bigger picture of what this job would look like, your questions show a deeper engagement in the role and company, much more so than asking basic questions such as “What’s the salary? What is the holiday allowance? What are the working hours?”.  It’s perfectly normal to write down your questions and take them into your interview to avoid forgetting any questions you wanted to know the answers to, so don’t feel as though you can’t do this.  Re-read the job description Spend time highlighting the responsibilities in the job description and thinking how your experience equips you to meet these. Try to prepare concrete examples from your past that back up why you’d be great for the role. How have you dealt with challenges or what successes have you had that you can link to how you’d be successful in this role? Try to come up with at least 5 solid examples or stories to talk through in the interview.   Write down questions you’re likely to be asked There are some questions you can almost guarantee on being asked such as “tell me about yourself” or “what is your biggest weakness”. It’s also likely you’ll be asked questions around your interest in the role and the company and why you applied. Be prepared to talk about numbers, in particular any significant impact that your previous projects have made on a business.  For each question you think of jot down a few notes or bullet points to build upon instead of writing out an entire answer and trying to remember it word for word.  Practice saying your answers Practising your answers out loud and looking in the mirror will help you clarify your answers and make you more comfortable during the interview. Try doing a mock interview with a friend or family member to help polish your delivery and boost your confidence in what you’re saying.  Dress accordingly Figure out what to wear to the interview by asking what the office dress code is before the interview. If the business has a business casual or business dress code, it’s appropriate to wear a suit for males and females to dress in smart business attire.  Make sure your outfit and shoes are clean and your bag/briefcase is emptied of any rubbish and packed with interview essentials: pens, a notepad, a copy of your CV, list of questions, mints, business card. These may seem obvious, but employers do take note and still make judgements based on how you present yourself.  If you’re looking to take the next step in your career, or if you’re looking for help with your next hiring process, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

Why Businesses Need To Put Fraud Prevention Front And Centre

If Fraudsters are anything, they are opportunists. Once the first new stories about COVID-19 started running, it wasn’t long until they were joined by tales of fraudsters selling face masks and hand sanitiser, asking panicked customers to transfer money and then disappearing without a trace.  And it’s not the first time we’ve seen this. Fraudsters are notoriously wise to periods of heightened sensitivity and uncertainty, often preying on the vulnerable. The 2008 financial crisis saw an increase in email-based phishing scams and a decade’s worth of technological advancements means that Fraud remains a many-headed beast.  Add into the mix a change in working styles and environments, and many businesses are more exposed to potential security breaches than they have been in years. Now, more than ever, companies need to make sure their Data is well protected and secure. THE FIRST LINE OF DEFENCE If you’re part of, or leading, a Fraud Prevention team, there are a number of ways you can support your business and keep on top of the situation. Here are just a few: Increase and update your investigation capacity. This team are the front line of your business’ Fraud defence team, interacting with customers daily and spotting new scams. During an uncertain period, retention and team stability is key. These are the people that understand the day-to-day Fraud challenges you face and will be essential in fighting any future challenges.  Sharing Fraud Prevention knowledge is key. Throughout this crisis, trends will be evolving quickly and working collaboratively across teams, and even other businesses, is the best way to combat this. We consistently hear from Fraud Managers that the key to beating Fraud is to share information and knowledge. Despite this, there is always a hesitation amongst companies to admit that they have been a victim to an attack. Perhaps now is the time to change this. Invest in Machine Learning and real time updates for your Fraud defences. Fraud technology has moved on from script writing in SQL and rule changes. Businesses need a real time reactive response and now is an important time to be embracing new technologies. There are a number AI-driven off the shelf packages available or, for a more bespoke solution, a Fraud Data Scientist can create something internally. Educate your team. It may seem simple, but the Fraud team can play a crucial role in minimising any potential risk from human-error. Educating employees on the risks they may face when working remotely, or what scams they need to look out for, is one of the most effective ways of fighting Fraud.  PREPARING YOUR BUSINESS Success in the fight against Fraud isn’t purely down to the group of individuals that make up the Fraud team. As a business, now is the time to be making decisions that can help you stay ahead of the Fraudsters. Here are some considerations: Consider investing in tech as an your immediate response. Not just to bolster your Fraud defences (although there are plenty of vendors offering AI-based solutions), but also technology for your employees to keep work as normal as possible such a sharing platforms, DevOps technology and video calling networks. One of the best ways to block some of the vulnerability loopholes fraudsters are trying to exploit is to keep working habits as close to normal as possible as you move to a remote solution. Be transparent with your customers. Consumers are being incredibly savvy and noting how businesses respond to the pandemic in a way that could have a big impact when normality returns. But they’re also being more empathetic and are willing to understand difficulties. For example, shopping delivery service Ocado were open and transparent when their system could not initially deal with demand. Having communicated the difficulties, worked through their issues and gone the extra mile to let customers know how they can be supported in this time, the received minimal backlash. There is an understanding that we’re all in this together. Finally, if you have the budget, continue to staff up - particularly in competitive fields such as Data Science. A lot of top Data professionals are currently at home and much more accessible than they have been in a long time. With a number of ways to remotely interview and onboard both permanent and contract staff, if you are able to get begin conversations with them now, you’ll have an edge in what will be a very competitive market come later in the year.  If you’re looking to take your next step in the world of Fraud, we may have a role for you, including a number of remote opportunities.  Or, if you’re looking to expand and build out your Fraud team, get in touch with one of expert consultants who will be able to advise on the best remote and long-term processes. 

Die Grippe, Schlangenbisse und das Covid-19 Virus: Jacob Glanville von Netflix’s "Pandemic"

Jacob Glanville aus der neuen Netflix Serie „Pandemic“, berichtet über die wegweisenden Fortschritte, welche er und sein Team bei Distributed Bio in der Welt der biotechnologisch hergestellten Medizin erzielt haben.  Diese Woche haben wir uns mit Distributed Bio’s CEO Jacob Glanville, Feldführer der fortgeschrittenen rechnerischen Immuntechnik der Biomedizin, zusammengesetzt. Die neue Netflix Serie "Pandemic“ gewährt einen Einblick in die Teams, welche derzeit alles versuchen, um einen globalen Ausbruch der Krankheit zu verhindern, und Glanville, ein sehr renommierter Experte mit einer unglaublichen Erfolgsbilanz.  Bevor er Distributed Bio gegründet hat, erlangte Jacob Glanville einen PhD der Universität Stanford und arbeitete vier Jahre als leitender Wissenschaftler bei Pfizer. Das Team, geführt von Sarah Ives, Geschäftsführerin Influenza Centivax bei Distributed Bio, entwickelt momentan eine neue universelle Art wegweisender Computertechnologie. Derzeit verwenden wir rechnerisches Ankoppeln mit hohem Durchsatz, um charakterisieren zu können, wie viele einzigartige Epitope auf der Oberfläche eines viralen Hüllproteins oder eines Pathogenproteins existieren können. Danach verwenden wir diverse Berechnungsmethoden, um die unterschiedlichen Elemente der jeweiligen Arten von viralen Kostenproteinen aus verschieden entwickelten Versionen desselben Pathogens identifizieren zu können. Dieses ist das Kernstück unserer Impfstofftechnologie. Wir verabreichen eine Reihe von sehr unterschiedlichen Varianten zeitgleich mit einer niedrigen Dosis, sodass die gemeinsam genutzten Stellen eine ausreichend hohe Dosis aufweisen, um darauf reagieren zu können." Diese Technik ermöglicht es Distributed Bio, Impfstoffe für fast jedes Virus schnell in einer sicheren Umgebung herzustellen. Zum Beispiel, aufgrund des jüngsten Ausbruchs des SARS-Coronavirus, arbeitetet Glanville mit dem US-Militär zusammen. Ein Programm der Weltgesundheitsorganisation ermöglicht die Herstellung von Pseudo-Virion-Versionen der Krankheit, welche untersucht werden können, ohne jegliches Risiko darzustellen: "Sie nehmen die Windpocken und lassen die Außenseite der Windpocken überlaufen, das Kostenprotein der schwerwiegenderen Krankheiten, wie das Coronavirus. Somit verhält es sich nun wie das Coronavirus und sieht äußerlich auch so aus. Es ist wie die knusprige äußere M&M Schale, ähnlich wie das Coronavirus, jedoch innerliche ist es eher wie weichen, klebrigen Schokolade, wie die Windpocken. Es ist nicht sonderlich gefährlich dadurch. Wir bauen eine Beziehung mit dem Militär auf, durch welche wir unsere Antikörperentdeckungsbibliothek in Verbindung mit Pseudovirionpartikeln verwenden können. So können wir schnell Antikörper entdecken, zum Beispiel gegen SARS, ohne SARS Risiko in unser Labor zu bringen.“ Jedoch beschränkt sich ihre Arbeit nicht nur auf die Bekämpfung von Viruserkrankungen. Eines der führenden Projekte von Distributed Bio konzentriert sich auf die Erschaffung eines universellen Gegengifts gegen Schlangenbisse. Die Notwendigkeit eines erschwinglichen Gegengifts mit einfachem Zugang ist hoch, da 80.000 bis 130.000 Menschen jedes Jahr durch Schlangenbisse, die meisten davon in Dritte-Welt-Ländern, getötet werden. "Es existieren ungefähr 550 Schlangenarten und jede hat 20 bis 70 Proteine. Es hört sich nach einer hohen Anzahl von Proteinen an, welche analysiert werden müssen. Aber wenn ich diese analysiere, werden 10 verschiedene, homologe Gruppen, die alle Schlangenarten teilen, deutlich." Nachdem herausgefunden wurde, dass ein universeller Ansatz sowohl möglich als auch realisierbar war, wie wurden die benötigten Antikörper entwickelt? "Unser Team, geleitet von Tim Friede, Geschäftsführer für Herpetologie bei Distributed Bio, Sawsan Youssef, Wissenschaftlicher Geschäftsführer und Raymond Newland, leitender Wissenschaftler, hat einen Mann gefunden, der Schlangen so sehr liebt, das er sich 17 Jahre lang Schlangengift aus aller Welt injiziert hat und dessen Blut haben wir testet. Wir haben Labor- und Berechnungsmethoden verwendet, um eine Reihe von Antikörpern zu identifizieren, welche eine gemeinsame Bestimmungsfaktoren aufweist.“ Bei einem Team, dessen Rollenspektrum sich von Dateningenieuren und Datenwissenschaftlern bis hin zu Bioinformatikspezialisten streckt, ist die Fähigkeit einer Zusammenarbeit besonders wichtig. Wie schafft Glanville solch ein kollaboratives Umfeld? "Ich versuche die Mitarbeiter so gut wie möglich weiterzubilden. Meiner Meinung nach werden Annahmefehler verringert, indem man die Mitarbeiter stetig weiterbildet. Ich denke der häufigste Grund, weshalb Missverständnisse und Fehler aufkommen, ist, da Mitarbeiter nicht verstehen, was ein anderer Mitarbeiter benötigt und was ihnen von der vorherigen Person weitergegeben wurde. Wenn Mitarbeiter im Stande sind, das Fachwissen ihrer Kollegen mit in die Arbeit einzubeziehen, wird dieses Risiko reduziert." Glanville ist in Guatemala aufgewachsen und ist sich dadurch der Notwendigkeit leicht verfügbarer und wirksamer Impfstoffe sehr bewusst. Besonders die westliche Welt wird immer vorsichtiger, was Infektionen angeht, aufgrund von der hohen Menge an Fehlinformationen, die sich derzeit im Umlauf befinden. Er versteht jedoch, dass dieses oft mit Vertrauen zusammenhängt. "Es ist schwierig, der Weltbevölkerung eine epidemiologische Empfehlung zu übermitteln. Oftmals werden keine Impfungen sind besser empfunden. Ich hoffe, dass ein effektiver Impfstoff dieses Fehldenken verschwinden lässt. Leider ist es derzeit noch das Problem einer Grippeimpfung, dass diese nur die Hälfte der Zeit funktioniert. Und das führt dazu, das Menschen anfangen, sich zu beschweren. Ich hoffe, dass bessere Impfstoffe und eine vernünftige Kommunikation dazu beitragen, dass dieses Fehldenken geändert wird." In Bezug auf unmittelbare Bedenken hinsichtlich der Auswirkungen des Coronavirus wendet er sich erneut der Frage der Zugänglichkeit zu: "Im Moment mache ich mir mehr Sorgen um Ebola. Es ist ein größeres Ausbruchsproblem und in einem Gebiet, das nur schlecht versorgt wird. Ich denke, China ist recht gut darin, medizinische Probleme zu lösen." Wenn Sie Ihr Team mit den Besten der Branche ausbauen möchten, wenden Sie sich an unsere Fachberater: Für unser deutsches Team rufen Sie bitte +49 30 217 899 21 oder +49 30 217 deutschlandinfo@harnham.com. Wenn Sie auf der Suche nach Ihrer nächsten Herausforderung sind und einem innovativen, weltweit führenden Unternehmen beitreten möchten, haben wir möglicherweise eine Rolle für Sie. Hier finden Sie unsere neusten Jobs. Pandemic ist jetzt auf Netflix verfügbar. Anbei der Trailer. 

Top Ten Tips: Video Interviewing for Data & Analytics Professionals

Large parts of the world may have moved to working remotely for the foreseeable future, but that doesn’t mean that their projects have ground to a halt. And, with Data & Analytics at the forefront of many businesses ongoing strategies, their Data teams are continuing to grow regardless.  As a result, we’ve seen a huge increase in the number of video interviews taking place as companies look to continue hiring and meet their growth and business goals.  For many, however, video interviewing will be an entirely new experience, one that throws a number of complications into the mix during an already unusual situation. With that in mind, we’ve put together our ten top tips for acing a video interview: UNDERSTAND THE TYPE OF VIDEO INTERVIEW “Live” interviews are ones where you’ll see another person on the end of the connection. These are typically conducted using Skype, Zoom or Google Hangout. For some interviews you’ll be recording your answers, expect these to be done using sites like Sparkhire. Ask your recruiter or contact with the business in advance so you know what type of video interview to expect. TEST YOUR INTERNET CONNECTION AND WEBCAM Test your connection for Skype, Zoom Google Hangout, or whichever interview platform you are specifically using. Do a test run to see how fast/slow your connection is to see if you will have any problems with the video that you may need to resolve beforehand. SOUND CHECK  Equally as important is how you sound. Having to repeat your answer because the interviewer couldn’t hear you will not only annoy the interviewer, it may disrupt your flow and throw you off guard. If possible, try not to use headphones, as they may make you look less professional (video interview or otherwise!), but audio quality is more important than appearance here, so check the audio in advance to be sure. CONNECT WITH YOUR INTERVIEWER IN ADVANCE  If you know who you’re interviewing with connect with them on LinkedIn beforehand or get their phone information. This is so you have a backup in case the video platform isn’t working and will save any last-minute panicking if the platform isn’t working. DRESS THE PART Just because the interview is over video doesn’t mean you don’t get dressed up for it. Dress how you would if you were having the interview face to face – first impressions count! Plus if you’re dressed smartly from head to toe it’ll help you feel best set up for success. LOOK BEHIND YOU  Interviewers can easily be distracted by what is happening behind you. If you don’t have a home office, use a room where you’ll have a wall or bookcase behind you which will look professional. REMOVE DISTRACTIONS Noise, music, children and pets can all be distractions to you and your interviewer. Be prepared to continue through the interview if your pet makes noise or your child barges in. Ideally if you can find a quiet space away from these distractions you won’t be interrupted. MAKE EYE CONTACT Interviews over video won’t replicate a live meeting. You have to proactively make sure you smile, make eye contact and speak clearly. Don’t fidget or make a lot of movement – if the connection is slow, you’ll appear fuzzy and out of focus. DON'T PREPARE AT THE LAST MINUTE You wouldn’t leave preparing to the last minute if you were meeting face to face so a video interview shouldn’t be different. Prepare your answers, questions to ask the interviewer and use post it notes if you need helpful reminders for video-specific tips (Look at the webcam! Smile! Speak clearly!). KEEP A GLASS OF WATER NEXT TO YOU It’s an ideal prop if you do need to take a couple of seconds to collect your thoughts before answering a question. Don’t substitute for a hot beverage (tea or coffee for example) as if you do spill you don’t want to be distracted by a burn or stain.  If you are looking for your next role, we may be able to help. Take a look at our latest jobs, where you will find a number of remote working opportunities.  Or, if you are looking to make a remote hire, get in touch with one of our expert consultants and we can help you manage the process. 

There’s Women At The Forefront Of Every Industry: A Q&A With Rachel Stuve

We recently had the opportunity to talk to Rachel Stuve, one of LinkedIn’s Top Voices in Data Science & Analytics, and a leading Director of Data teams. An expert in her field, Stuve has a wide breadth of experience. Having attended college in automotive-heavy Michigan, her first role was analysing the auto-industry at Chrysler. Shortly after she moved into local government, digitising and integrating their law enforcement processes before working on a state-wide Data-sharing initiative.  Most recently, however, Stuve has been focusing her efforts in Healthcare. While it might seem to many as a highly-specialised, inaccessible industry, Stuve disagrees. “It’s all about transferable skills,” she says. “You may be looking at different sets of Data with a healthcare provider but, essentially, the analysis follows the same principles”. Despite this, Stuve does admit that there are some hurdles to overcome, particularly when it comes to terminology.  “Admittedly the jargon does take some getting used to, and there is a lot of it.”  But the main differences are less scientific and more to do with infrastructure. Unlike like many Data-led industries, Health Insurers do not deliver directly to consumers. In fact, their main relationship is with Healthcare Providers.  “It’s not the same as getting a mortgage, you don’t approach your insurer to be provided with care. Your direct service is with the Healthcare provider, the hospital, or whoever, and it’s the insurer’s job to cover the payments. Part of the challenge is working out which providers offer the best value for money and, also, which ones offer quality care”. This means managing a team comprised of both Data Scientists and Epidemiologist, specialists who can better identify which treatments provide the most success, at the lowest cost. So, how can you get a team with different backgrounds and approaches to work in harmony with one another? “So much of a project’s success relies on agreeing to the right goals at the start. If you can get everyone to agree on what success looks like, be it a 10, 20% profit increase or whatever, you know you’re all working towards the same thing. Sure, you may have some debate around statistical conversations, but ultimately you’re all pulling in the same direction. "Stuve also stresses the importance of including the right people at the right stage of each project. Too often end-users are not included in the early stages of Data projects, leading to huge gaps in knowledge. Stuve notes: “If those who have true knowledge of what they need from a project are left out of the initial scoping, things will almost certainly be missed” In addition to her work in Healthcare, Stuve also invests in female-led start-ups with her work at Golden Seeds, something that is close to her heart.  “I love Golden Seeds. There have been numerous studies that show that female-run businesses produce higher returns, and yet they only receive a fraction of the investment that male-led businesses do.” She points to a recent article in the Harvard Business Review as to why this may be. According to the article, there is an inherent gender bias in the investment process where male entrepreneurs are asked about the potential of their businesses. Female entrepreneurs, on the other hand, were more likely to be asked purely risk-mitigating questions.  “People invest in optimism, so if you aren’t allowing an entrepreneur to sell you the dream, you’re far less likely to invest in them”.  Stuve also believes that there’s a perception that female-led businesses are less likely to be innovative: “I want to change the idea that these businesses are, for want of a better word, ‘girly’ and purely focused on clothes, food and retail. This is not the case from what I’ve seen, and women are at the forefront of all sorts of industries from biotech, to energy, to any number of specialisms”.  So, what does she look for when investing? “Sure I’m looking for an innovative idea that fulfils a business need, but I’m also looking to invest in the person. Are they realistic? Are they are strong leader? Do they know their own weaknesses and have they built up a team around them who can pick up where they’re not as strong?” “There’s also, unfortunately, a double-standard when it comes to the perception of male and female leaders. This means how they carry themselves makes a big difference, particularly if they’re looking for further investment in the future.” Stuve is well aware of the difficulties women face in male-dominated industries, having found herself as the sole female in many of her teams, increasingly so as she progressed into management. Fortunately, she sees light at the end of the tunnel: “Companies are beginning to see the value in broadening the diversity of their teams and there’s definitely been a shift in the corporate conversation around this.”  “Also, if you look for it, there is a fantastic network of women in Data out there. Reaching out tends to have this snowballing effect as well. You connect with one person, who introduces you to another, who introduces you to another, and soon you discover this amazing community of exceptional women”.  If you’d like to hear more from Rachel, you can follow her LinkedIn for regular updates and ideas.  For more information on the current states of Diversity in Data & Analytics, you can download our report on the subject here.  If you’re looking to build out your team or for a new opportunity, you can get in touch with one of our expert consultants or view our latest opportunities here. 

Why You Should Always Be Learning In Data Science: Tips From Kevin Tran

Last month we sat down with Kevin Tran, a Senior Data Scientist at Stanford University, to chat about Data Science trends, improvements in the industry, and his top tips for success in the market.  As one of LinkedIn’s Top Voices of 2019 within Data & Analytics. his thoughts on the industry regularly garner hundreds of responses, with debates and discussions bubbling up in the comments from colleagues eager to offer their input.  This online reputation has allowed him to make a name for himself, building out his own little corner of the internet with his expertise. But for Tran, it’s never been about popularity. “It’s not about the numbers,” he says without hesitation. “I don’t care about posting things just to see the number of likes go up.” His goal is always connection, to speak with others and learn from them while teaching from his own background. He’s got plenty of stories from his own experiences. For him, sharing is a powerful way to lead others down a path he himself is still discovering.  When asked about the most important lesson he’s learned in the industry, he says it all boils down to staying open to new ideas.  “You have to continue to learn, and you have to learn how to learn. If you stop learning, you’ll become obsolete pretty soon, particularly in Data Science. These technologies are evolving every day. Syntax changes, model frameworks change, and you have to constantly keep yourself updated.”  He believes that one of the best ways to do that is through open discussion. His process is to share in order to help others. When he has a realisation, he wants to set it in front of others to pass along what he’s learned; he wants to see how others react to the same problem, if they agree or see a different angle. It’s vital to consider what you needed to know at that stage. Additionally, this exchange of ideas allows Tran to learn from how others tackle the same problems, as well as get a glimpse into other challenges he may have not yet encountered.  “When I mentor people, I’m still learning, myself,” Tran confesses. “There’s so much out there to learn, you can’t know it all. Data Science is so broad." At the end of the day, it all comes down to helping each other and bringing humanity back to the forefront. In fact, this was his biggest advice for both how to improve the industry and how to succeed in it. It’s a point he comes back to with some regularity in his writing. “It doesn’t matter how smart you are, stay humble and respect everyone,” one post reads. “Everyone can teach you something you don’t know.” Treating people well, understanding their needs, and consciously working to see them as people instead of numbers or titles—this, Tran argues, is how you succeed in the business. To learn and grow, you must work with people, especially people with different skills and mindsets. Navigating your career is not all technical, even in the world of Data. “The thing that cannot be automated is having a heart,” he tells me sagely. Beyond this, Tran stresses the need for a solid foundation. The one thing you can’t afford to do is take shortcuts. You have to learn the practicalities and how to apply them, but to be strong in theory as well.  Understanding what is happening underneath the code will keep you moving forward. He compares knowing the tools to learning math with a calculator. “If you take the calculator away, you still need to be able to do the work. You need the underlying skills too, so that when you’re in a situation without the calculator, you can still provide solutions.” By constantly striving to collaborate and improve, Tran believes the Data industry has the best chance of innovating successfully.  If you’re looking for a new challenge in an innovative and collaborative environment, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

Coronavirus Update: What to expect from Harnham

As we learn more about COVID-19, we want to inform you of the proactive measures Harnham have taken to ensure the health and safety of our employees, while continuing to provide the best possible service to you.  The majority of our service offering will be unaffected by the current situation. All staff are continuing to work remotely and are on hand to support you, although you may experience slight delays in communication or find our phone lines busy. In these instances, we'd ask that you contact the member of the Harnham team that you were last in contact with directly. If you need to find their details, you can contact them via their online profile. Alternatively, you can also contact us via our social media channels and directly via email to our main inbox (UK/EU and USA).  Our Operations and Technology team have been working around the clock over the past weeks to ensure that we are able to continue running processes virtually. This has ensured that we are able to provide our clients with virtual meeting spaces, alongside the opportunity to conduct video interviews and calls without the need for face to face interaction.  We are working with a number of businesses who are continuing to hire, supporting them as they begin putting in place alternative processes. We will be in contact with all candidates who are currently in any process to update on the current situation or any change to process.  If you are currently looking for a new role, all our open vacancies have been updated on our website which you can view here.  In the coming weeks our Marketing Team will be running a number of events such as webinars and online Q&A sessions. I would advise that if you are not already following us on Social Media (Twitter and LinkedIn), that you do so to ensure you don’t miss these. We are also working to provide a range of comprehensive guides covering the challenges that you may face in the current climate.  I’d also like to add, if you have yet to take part in the Harnham 2020 Salary Survey please take a moment to do so, we will be extending this for a further two weeks due to unprecedented demand. All those that take part will be the first to receive a copy of the report.  In the meantime, we're running as close to business as normal as we possibly can, and are still here to support you with any hiring or job-seeking needs. We hope that you are able to look after yourself through this trying time and we look forward to working closely together again when normality returns.  

How Computer Vision Is Streamlining Manufacturing

Since the Ford Motor Company first introduced the assembly line for car production, automation has been part of the manufacturing industry. Over 100 years later, Computer Vision adds another layer to streamlined processes. Industrial robots. Drones. Automation. With the adoption of AI technologies and its connective capabilities, we’re in the next age of Smart Manufacturing. Demand is led by supply and, as consumers demand more, manufacturers are constantly evolving to ensure their processes are efficient and safe. The implementation of machines allows them to make sure quality control measures are in place and catch issues before breakdowns occur. This verification of output far outpaces the human eye and opens up opportunities for more creative thinking.  Working Hand In (Robotic) Hand While there may still be some element of fear regarding machines taking over jobs, this isn’t the intent. Ultimately, the idea is for humans and machines to partner for more streamlined and efficient processes within the industry. The role of machines is to continue the automation of processes using image recognition, gathering insights from AI-driven Analytics solutions, and optimising operations across facilities. We continue to retain oversight of these processes, but are now also free to focus on higher-value tasks at the same time, allowing strategic and creative thinking to take the lead.  Computer Vision is playing a crucial role in the implementation of AI in manufacturing and its use is estimated to grow more than 45% by 2025. Why? Here are a few reasons: Quality inspectionPredictive maintenanceDefect reductionProductivity improvement Human-machine partnerships through the adoption of AI, cloud-based technologies, and Computer Vision are helping to prepare facilities to become networked factories. Not unlike the un-siloed Data teams working throughout a variety of industries, the factory will also link their teams. From design to supply chain, the production line to quality control; the coming years will see continued growth in the output and efficiency of today’s manufacturer. Looking Out For Bias However, there is one area in which Computer Vision remains lacking. Navigating visual images still contains within it a bias which can be detrimental to some production output use cases. Think cars, wearable devices, or uniforms. The biases and stereotypes found most often in Computer Vision algorithms are three attributes protected by anti-discrimination law; gender, skin colour, and age. To help combat these biases and make imageable visuals more easily identifiable, two computer scientists embarked upon a research project.  What they found was that not only were there biases in these areas but some visual clues still posed problems.  However, the images used to train Computer Vision technologies can determine the differences. Not just in people, but in landscape and objects as well. By crowdsourcing correct categorisations, automating image collection, and more aptly defining words to negate stereotypical phrasings, researchers are striving toward a bias-free image capture. Seeking Out Business Goals In the last few years, Computer Vision has made great strides in uniting technologies to streamline the manufacturing process. As researchers work to reduce bias in computer vision and AI, machines become ever more essential for meeting business goals. Factories with smart manufacturing systems can more quickly process inefficiencies with improved accuracy. In 2017, sales of Computer Vision and automation systems grew 14.6% over the previous year to $2.633 billion. All industries are noticing the benefits of Computer Vision as an essential system but, like the Ford Motor Company in the early 20th century, manufacturing looks once again set to lead the world in innovation.  Ready to take the next step in your career? Whether you’re interested in AI, Big Data and Analytics, Computer Vision or more, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to find out more.  

What Does The Fourth Industrial Revolution Look Like?

We’re in the next stage of the fourth Industrial Revolution and technologies continue to merge. No longer is advancement as simple as adding “tech” to the end of a word - sorry Fintech, InsurTech, HRTech, and the rest. Now technologies stand together as each becomes a separate piece of how tech operates in the business world. AI and IoT have merged to become AIoT. Data is as much commodity as it is information to fuel business growth. Computer Vision partnered with AI is teaching computers to convert their ones and zeros to images humans take for granted.   In a word, it’s a transformative time for every industry and every industry is taking advantage of the benefits in one way or another. Smart manufacturing. Human Resources. Marketing. Even insurance has joined the party. But, with so many advancements, we thought we’d take a look at just the tip of the iceberg, starting with A, B, and C.  AI Meets IoT  We’ve all heard how AI and the Internet of Things (wearable and smart devices etc.) are being used in the Health sector. With the kind of real-time Data available, patients, insurers, and medical professionals can map out health plans based on wearable devices to track patient health and encourage preventative care.  Indeed, one insurance company is embracing these Data trends to ramp up the speed and efficiency of their data. Using Machine Learning and IoT sensors to develop an AI-based solution, customer information is used to match clients with the right policies tailored to their needs.  Car insurance is another industry to benefit. Insurers are able to collect real-time driving data which they can analyse to determine risk or offer discounted policies for good driving. This kind of information can also be used to revisit and reconstruct accident scenes to figure out what happened and who’s at fault.  Big Data, Big Money We’ve all heard the phrase ‘Data Is The New Oil’ by now, which I’m sure we can all agree, just means Data is a resource everybody wants and is willing to pay a lot for. But the differences between Data and Oil are two-fold; Data has the potential to be infinite, and it tells us about what oil cannot; the human experience.  Cloud technologies, edge hardware, and the IoT have helped shape the digitisation of objects, people, and organisations. From sensors to wearable devices, more and more data is being collected, allowing us to be more connected than ever before. It’s also providing more information to the tech giants than ever before. For example, Amazon’s Ring doorbell is logging every motion around it and can pinpoint the time to millisecond.   Add these technologies to Natural Language Processing (NLP) and watch the world around us draw value from and understand our Data like never before. The wave of Big Data value shows no signs of slowing down. Computer Vision in Business In the last few years, Computer Vision has been making great strides in the business world. Yet the Data required for processing power and memory can still be impacted by image quality. The opportunities  are alive with possibility and, from small businesses to enterprise solutions, Computer Vision has seen a variety of industries finding practical business uses.  Below are just a few additional areas Computer Vision is making its mark. Facial Recognition – providing surveillance and security systems in such areas as police work, payment portals, and retail stores.Digital Marketing – sorting and analysing online images to target ad campaigns to the right audiences.Financial Institutions –preventing fraud, allowing mobile deposits, analysing handwriting, and beyond. With the global market for fourth industry technologies predicted to be between $17.4 billion and $48.32 billion by 2023, now is the time to find your focus within the industry.  Ready to take the next step in your career? Whether you’re interested in AI, Big Data and Analytics, Computer Vision or more, we may have a role for you. Take a look at our current opportunities or get in touch with one of our expert consultants to find out more.  

The Top Trends to Watch in Big Data & Analytics

Having a Data Strategy Plan for your business is no longer a nice-to-have. It is a must have, and in most circles is mission-critical to success. Where terms used to be fused (e.g. AdTech, MarTech, FinTech, etc.), infrastructures are now merging to create better and more powerful opportunities to better serve business and the day-to-day tasks of our personal lives. Data continues to change the landscape of our world and, as we look forward to the future, this shows no sign of slowing. Here are some of the biggest trends shaping Data Analytics right now: A Smart World: IoT, Conversational Analytics, and NLP It’s estimated that by 2020, there will be around 30 billion IoT connected devices. Think beyond smart houses and smart cars to video doorbells, smart refrigerators and toasters, setting your thermostat by remote and beyond. As this cadence of progress marches forward, IoT will no longer be a hardware challenge, but a data challenge.  And according to a Gartner report on Data Analytics trends, it’s estimated 50 percent of analytical queries will be generated via search, voice, or NLP by 2020. Will this make Data Analytics more approachable for the everyman?  Augmented Analytics Is Augmented Analytics poised to be the next big thing? With a market valued at US$8.4 billion just a couple of years ago, it’s estimated to grow to US$22.4 billion by 2025. Using Machine Learning and NLP to automate Data preparation for sharing, Augmented Analytics could redefine the way we approach our Data. Cloud Continues to Come of Age Cloud IT infrastructure may already be mainstream, but with spending expected to reach US$82.9 billion by 2022, and with over half all software, services, and technology expected to connect to the Cloud in the same timeframe, expect the conversation around the Cloud to keep growing. Data Science Roles Evolve into Specialties  Frequently, the term Data Scientist is used to refer to anything to do with Data. From gathering and collecting to developing metrics and offering Data insights and forecasting. But as Data teams became less siloed and IT infrastructures became more specialised, the role has evolved into more specific functions, often with a Machine Learning or NLP focus. However, this evolution didn’t stop at these specifics.  It’s even made room for those with no programming background. From Marketing Analysts who focus on the customer journey to Business Analysts and Communications Directors who can utilize their soft skills to explain Data professionals’ findings. Whether you’ve got a programming background or an interest in how Data fuels business, the world of Data has opened up.  Have we reached the end of the beginning? As we find ourselves on the cusp of the next generation of Data & Analytics in both our personal and professional lives, we spoke to Daniel Levine, Trends Expert and Keynote Speaker to gather his thoughts. Here’s what he had to say: "The biggest overall trend I'm seeing for Data in the coming decade is all about ownership, transparency, privacy, and governance. Data famously wants to be free, but the opposite has happened as companies across the industrial spectrum have scooped it up and siloed it away. The 2020s will create unprecedented amounts of Data from biometric recognition, deep learning, digital streaming, smart appliances and more. And ownership will be shared with -- if not controlled by -- individuals rather than governments and corporations. Blockchain will play a pivotal role in this, offering individuals the ability to safely take control of their own Data.  Many governments, led by the liberal democracies of Europe, will enact legislation that aligns with new realities and new technologies that empower both institutions and individuals to understand and control their own Data. The frightening alternative that, when it comes to Big Data, we have reached the end of the beginning whereby a totalitarian dystopian future is nigh." If you’re interested in Big Data & Analytics, we may have a role for you. Take a look at our current opportunities or get in touch with one of our expert consultants to find out more.   

What defines a Data Architect?

Data Analyst. Data Wrangler. Data Architect? If you like pulling together threads of a company’s Data into one cohesive point, you may want to consider a Data Architect role. But what exactly is a Data Architect and how does it differ from a Data Engineer? Data Architect vs. Data Engineer As businesses continue to combine their Data and business strategies into one, they are beginning to understand to the need for a variety of Data Analysts. But as important as it is to have someone build your platform and begin pipeline processes, there is also need for someone with vision. Someone who can see patterns and designs. Someone who has end-to-end vision and can see how the patterns flow through your processes. This is your Data Architect. Data Engineers, on the other hand, lay the foundation for your Data platform. They draft the blueprint. After all, you can’t build a house without a blueprint first, right? The Data Engineer is at the beginning of the process, so the rest of the team can do their parts. But it’s the Data Architect who pulls it all together. THE ROLE OF THE DATA ARCHITECT  If you’re considering your next career move and wondering if Data Architecture is for you, here are some typical requirements. A typical Data Architect will: Meet with stakeholders to understand business needs and translate them into technical requirements using ETL techniques to develop Data ArchitectureUnderstand their full Data lifecycle to provide technical architecture leadershipDesign a real-time data pipeline ecosystem and how to make it scalable usingDevelop Big Data Architecture in an AWS environmentBe educated to a degree level in a numerate discipline (Mathematics, Statistics, Computer Science, Computer Engineering)• Have proven experience in a commercial environmentHave advanced Cloud Computing Ecosystem experience with AWS (GCP or Azure also considered)Have proven Big Data Ecosystem experienceHave proven Big Data Architecture experience in a commercial environment Have proven Data Engineering experience in a commercial environment Though the likes of Google, IBM, and others have ramped up their education efforts, and online courses traditional universities offer a variety of Data Science degrees, there is still a shortage of professionals in the industry. So can businesses simplify and automate processes without the right people in place? Businesses Step Up Their Data Strategies Though there are easier ways to get the information a business needs through rented predictive modelling or an already drafted Data Science model, it doesn’t give the true value of Data. Add in new regulations, requirements, and new Data which offer new insights, and the impact on business is profound.   It’s time for business to start ensuring that their Data teams are treated as critically as possible. Time to lay a path of progression, a pipeline, of systems and processes for the creation and production of Data. After all, simply optimising your Data will only get you so far. Enterprise-wide Data systems are more than wrangling and analysing Data.  Most importantly, businesses need to ensure they have the right people in place. They also need to understand what they need and why they need it. This is a key part of Data Strategy and with the right people in place, can put your business ahead of the competition.  Digging Deeper into Requirements for Top Talent While the standard requirements for a Data professional are to be educated to a degree level in things like Computer Science and Mathematics, technical skills, and experience within certain industries, for the natural progression from Data Analyst to Data Architect, there’s a bit more nuance to consider. Whether your business is just getting started in Data Science or you’re ready to start growing an existing team, there are some things you may want to focus on when looking for your Data Architect role. Define and determine how to keep projects streamlined with repeatable processes. Pivot between guiding team members through the pipeline and explaining insights to executives and stakeholders. Determine the right format for the right project. Determine when and when not to use automation to integrate Data. Visualise and extract models to predict future events and describe the process. In other words, be able to interpret Data to ensure reliability of the best approach. With the right talent in place, your teams can collaborate and build on their shared expertise to ensure Data is analysed and understood to the best benefit of your business. If you like solving puzzles, pulling disparate threads together into organised systems, and have experience as analysing and collecting Data, we may have a role for you.  Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.  

3 Ways Machine Learning Is Benefiting Your Healthcare

With Data-led roles leading the list in the World Economic Forum’s ‘Jobs of the Future’ report, it is no surprise that Data Science continues to be the main driving force behind a number of technological advancements. From the Natural Language Processing (NLP) that powers your Google Assistant, to Computer Vision identifying scanning pictures for specific objects and the Deep Learning techniques exploring the capability of computers to become “human”, innovation is everywhere.  It’s unsurprising, then, that the world of healthcare is fascinated by the possibilities Data Science can offer,  possibilities which could not only make your and my life better, but also save several thousands of lives around the world. To just scrape the surface, here are three examples of how Machine Learning (ML) techniques are being used to benefit our healthcare.  COMPUTER VISION FOR IMAGING DIAGNOSTICS  Have you ever had a broken leg or arm and saw a x-ray scan of your fracture? Can you remember how the doctor described the kind of fracture to you and explained where exactly you can see it in the picture? The same thing that your doctor did a few years ago, can now be done by an algorithm that will identify the type of fracture, and provide insights into how you should treat it. And it’s not just fractures; Google's AI DeepMind can spot breast cancer as well as your radiologist. By feeding a Machine Learning model the mammograms of 76,000 British women, Google’s engineers taught the system to spot breast cancer in a screen scan. The result? A system as accurate as any radiologist.  We‘ve already reached the point where Machine Learning and AI can no longer just outsmart us at a board game, but can benefit our everyday lives, including in as sensitive use-cases as the healthcare industry. NLP AS YOUR PERSONAL HEALTH ASSISTANT  When we go to our GP, we go to see someone with a medical education and clinical understanding who can evaluate our health problems. We go there because we trust in the education of this person and their ability to give us the best information possible. However, thanks to the rise of the internet, we’ve turned to search engines and WebMD to self-diagnose online, often reading blogs and forums that will convince us we have cancer instead of a common cold.  Fortunately, technology has advanced to the point where it can assist with an on-the-spot (much more accurate) evaluation of your medical condition. By conversing with an AI, like the one from Babylon Health, we can gain insights into possible health problem, define the next steps we need to take and know whether or not we need to see a doctor in person.  There’s no need to wait for opening times or to sit bored in a waiting room. Easy access from your phone democratises the process and advice can be received by anyone, at any time.    DEEP LEARNING DRAWS CONCLUSIONS BETWEEN MEDICAL STUDIES Despite their extensive qualifications, even medical researchers can feel overwhelmed by the sheer amount of Insights and Data that are gathered around the world in hospitals, labs, and across various studies. No wonder it’s not uncommon for important Insights and Data to get forgotten in the mix. Once again, Machine Learning can help us out. Instead of getting lost in a sea of medical data, ML algorithms can dig deep and find the information medical researchers really need. By efficiently sifting a through vast amounts of medical data, combining certain datasets and providing insights, ML sources ways for treatments to be improved, medicines to be altered, and, as a result, can save lives. And this is only the beginning. As Machine Learning continues to improve we can expect huge advances in the following years, from robotic surgery to automated hospitals and beyond. If you’re an expert in Machine Learning, we may have a job for you. Take a look at our latest opportunities of get in touch with one of our expert consultants to find out more. 

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