With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
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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 Blogs & News portal or check out our recent posts below.
Since Dolly the sheep was first cloned, humans have had a love-hate relationship with machines. Ok, maybe even before we asked a machine to make a living thing. In a variety of industries, machine learning systems, AI, and robotics are taking on the routine, mundane tasks once reserved for humans. But they’re doing this not to take away from humans, but to give them an opportunity to operate at a higher, creative level. So, when you’re modelling the mind using Machine Learning and Computational Data in Neuroscience for mind blowing breakthroughs, we sit up and pay attention. When it comes neuroscience, the benefits far outweigh the pitfalls. Just ask the researchers in China, who’ve developed a way to spot whether or not a child has autism from imaging the back of their eye. Other neurological orders such as dementia and Alzheimer’s falls under the computational neuroscience spectrum as well. From the 1970s to today, computational biology, using analytical, mathematical modelling, and simulation techniques to study behavioral and biological systems has evolved into a variety of subgenres. And it's within these subgenres we get a sneak peek into the mind of man that creates computers that can understand the mind of man. Can you wrap your head around it? Engineering the Mind – Mathematical Relationships The Life Sciences, Biostatistics, and Computational Biology all play a role in physical and mental health care. In seeking to understand the makings of the human mind, to study its syntactic rules, and to help explain how we think, human and machine have come together again. This time in the form of Computational Psychiatry. It’s here we realize our computational theories have often mirrored what we hoped to accomplish in building computers that could think with reason and logic. By understanding how we think, how the brain performs, and how it solves problems, can also help us to identify what we see as abnormalities of the mind – autism, schizophrenia, Alzheimer’s, dementia, and Parkinson’s disease just to name a few. At its heart, the fundamental message is that the brain’s way solving of inferred problems can be useful in determining hypotheses around neurological disorders. Even within these subgenres there are varying degrees of theoretical concepts and with the data Computational Biologists and Computational Psychiatrists are able to conduct to navigate the complex inner workings of the brain. But much like the gathering, collecting, and analyzing of the data for the pandemic, the same can be done for in the mental health arena. Not the least of these theorems newly determined comes from a new theoretical model in the journal Medical Hypotheses. In it, T.A. Meridian McDonald, PhD, a research instructor in Neurology at Vanderbilt University Medical Center describes the positive traits of autism. These positive traits she puts forth include but are not limited to increased attention, increased memory, increase differences in sensory and perception. Building Computational Relationships Building relationships between neurobiology, environment, and mental signals in computational terms provides a cognitive model to understand the current state of one’s environment. It’s this building of relationships upon which human minds and the inner workings of the machine come together for the common good. There are positives in the negative. Mindset shifts aren’t just for learning how to work online or be more mindful, but are how best to present, and put your best foot forward. If you’re interested in Life Science Analytics, Computational Biology, Decision Science, Machine Learning, or Robotics just to name a few, Harnham may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to firstname.lastname@example.org.
28. January 2021
As states begin to lift restrictions, projections of cases and deaths, are expected to rise. Racing against the clock are scientists, Predictive Data Modelers, Bioinformatics Statisticians, and AI. Together with Machine Learning platforms, they’re working to find a solution to not only flatten the curve, but stop the virus in its tracks. And the first line of defense they’re turning to? AI. AI as First Line of Defense Against Covid-19 Once a challenge to humans as taking over jobs, AI is now at the center of cutting-edge technologies to track the pandemic. Combined with other technologies such as machine learning, bioinformatics, and other Data solutions, AI has become a first line of defense against the outbreak. With the ability to filter through massive Data sets at a speed unmatched by humans, AI is working to crack the code of how best to treat Covid-19 patients. And to help flatten the curve? Some companies are using surveillance systems to help determine hotspots, track contagion points, and help to slow the spread of the virus. Experts understand computation prowess alone won’t solve this disease. But its speediness will ratchet up the information so we can avoid years of trial and error. AI and Big Data can help give research groups, pharmaceutical firms, and even private businesses, who’ve tossed their hat into the ring to help fight this pandemic, a leg up. Healthcare Analytics Enters the Fight While there is a risk to Data given to algorithms, some private sector businesses are willing to take the chance. This kind of information can help to predict future hotspots to catch the virus in its tracks and work to ensure it doesn’t spread out from these localizations. Using the technologies we have in place like AI, Machine Learning platforms, Big Data, and Bioinformatics can help to speed up treatment, improve vaccines and pharmaceuticals, and offers near real-time solutions. As with anything involving large amounts of Data given directly into a machine, there is an urge to be cautious. Hotspots on a map can pinpoint where the worst or newest cases have sprung up. This insight gives experts a 360-degree view of patients using Machine Learning. Using a wide array of healthcare Data such as X-rays, ER visits, medical codes, and more, a new tracking system shows hidden areas where outbreaks could occur but may not be expected catching the virus in its tracks. Monitoring is Critical as States Lift Restrictions Though coronavirus is not the flu, flu season isn’t taking any time off. So as the two viruses collide, it’s more important than ever to monitor the spread. This can ensure hotspots are pinpointed early, efforts to flatten the curve, and monitor flare ups should the arise as the country begins to reopen. Experts are working around the clock and using this clinical Data to inform their models. Predictive modeling, AI surveillance systems, and Bioinformatics used in healthcare Data are working together to find a cure. It’s important humans do their part, too. This just might be a best case scenario of machine and human working in cooperation toward a common goal. But one thing this world needs at the speed of light beyond AI to syphon through Data? Professionals who have the technical prowess to understand the machines and the human skills of communication to explain the analyses. One Final Thought… Business processes have shifted online, looking for your next job has become more daunting than ever before. But here’s the good news. Leaders, hiring managers, recruiters, and prospective employees are all navigating a new way of doing business and finding talent to keep those businesses running. In the wake of work-from-home policies, remote working, and the shifting landscape of working outside the office, technology careers are prime opportunities to both gain increased knowledge in your chosen field or begin your career path. If you’re interested in Big Data & Analytics or other Data professional opportunities, check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to email@example.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to firstname.lastname@example.org.
07. May 2020
Ever wondered how your email system knows which emails to show you and which to put in your junk or spam folder? Enter Machine Learning. It learns what you open and read and after a time can differentiate what you ignore, toss, or move to spam. Now imagine that same type of learning in the life sciences. As scientific advances move toward Data and Machine Learning to scale their knowledge, you can imagine the possibilities. After all, as you read this, trends in the life sciences, specifically with an eye toward bioinformatics showcase machine learning such as genome sequencing and the evolutionary of tree structures. Human and Machine Learning with a Common Goal There has been so much data provided over the past few decades, no mere mortal could possibly collect and analyze it all. It is beyond the ability of human researchers to effectively examine and process such massive amounts of information without a computer’s help. So, machines must learn the algorithms and they do so in any number of ways. For the most part, it’s a comparison of what we know, or is already in a databank, with the information we have and don’t yet know. Unrecognized genes are identified by machines taught their function. The Future is Bright Machine Learning is giving other fields within the life sciences both roots and wings. Imagine scientists being able to gain insight and learn from early detection predictions. This type of knowledge is already in play using neuroimaging techniques for CT and MRI capabilities. This is useful on a number of levels, not the least of which is in brain function; think Alzheimer’s Research, for example. The hurdle? It isn’t the availability of such vast amounts of data, but the available computing resources. Add to that, humans will be the ones to check and counter-check validity which can in turn become more time-consuming and labor intensive than the computer’s original analysis. And it’s this hurdle which leads to a caveat emptor, or “buyer beware” of sorts. Caveat Emptor: Continue to Question Your Predictions In other words, how much can you trust the discoveries made using Machine Learning techniques in bioinformatics? The answer? Never assume. Always double check. Verify. But as you do so, know this. Work is already in progress for next-generation systems which can assess their own work. Some discoveries cannot be reproduced. Why? Sometimes it’s more about asking the right question. Currently, a machine might look at two different clusters of data and see that they’re completely different. Rather than state the differences, we’re still working on a system that has the machine asking a different kind of question. You might think of it as a more human question that goes a bit deeper. Imagine a machine that might say something noting the fact that some of the data is grouped together, but if different, it might say while it sees similarities, but am uncertain about these other groups of data. They’re not quite the same, but they’re close. Machine Learning is intended to learn from itself, from its users, and from its predictions. Though a branch of statistics and computer science, it isn’t held to following explicit instructions. Like humans, it learns from data albeit at a much faster rate of speed. And its possibilities are only getting started. Want to see where Bioinformatics can take your career? We may have a role for you. If you’re interested in Big Data and Analytics, take a look at our 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.
12. September 2019
One of the latest technologies to emerge to disrupt an industry is Biotechnology. This industry is booming and is no longer confined to universities and research labs. These are the people who build drugs to combat diseases and are expected to comprise a quarter of the market by 2020, less than 6 months from now. So, what does that mean for HR? A Streak of Lightning Across the Life Sciences Biotechnology has grown at an impressive 5% across revenue streams, number of businesses, and number of employees. It is a lightning streak across the Life Sciences and shows no signs of slowing down. In a field expected to corner a quarter of the market as soon as next year, it’s important to have the right people in place. We already know there is a skills gap in the Data Science industry, but the predictions show it's time to upskill the current workforce. Companies will need people who have the right skills and can implement them into action. Technology has disrupted every industry and R&D is no different. This means work life is being redesigned as the Biotech industry demands not only technical and Life Science skills, but also more human skills. The challenge is ensuring businesses understand the impact these technologies will have now, and in the future. If they don’t act, their business could stagnate. It’s important executives see applications at work and implement the changes needed to “keep up with the Joneses” of the tech world. In other words, leaders must find a balance between rapidly advancing technologies and the human insight those technologies provide. Redesign Your Ideal Candidate While digital and analytical skills should be standard for just about any industry, there are other things to consider when interviewing. Hiring Managers, recruiters, and businesses over all, will also be looking for the following ImaginationCuriosityEmotional Intelligence You may not be a doctor exactly, but do still have to deal with people. Organizations will need employees who not only ask why, but take the steps to find the solution, and at the same time can navigate an emotionally charged project such any client-facing research when discussing cancer therapies, for example. Transferable Skills are Key If you pivot well and can learn and understand projects on a dime, then this is a good industry for you. If you’re a business and you want to scale up quickly, it may be best to upskill or reskill, your current employees. With talent scarce in the market, this may be the best solution for you. Building transferable skills, being flexible, and having a strong academic background will help, too. Companies actively working to skill their workforce to work with Machine Learning and Artificial Intelligence technologies are just a few of the trends coursing through the Biotech industry. Add to that the myriad researchers, corporations, and governments focused on combatting diseases using available technologies, and its expected growth could make it one of the most efficient and prosperous industries in the digital landscape. Making HR Data Work for You Businesses are using HR data to see how they can get a deeper understanding of employees as a whole. Are they overwhelmed? Do they need to rest? Do they need to be challenged? Are they bored? How can you, as a business, help them to enhance not only their performance, but that of your business. Finding exciting new recruitment channels Much like you know to go where your customers are, the same holds true today when you’re trying to fill a role. Focus your efforts are on where the talent is, don’t wait for them to come to you. And with the average recruitment process averaging 71 days, the name of the game is “don’t delay” for your perfect candidate may have already moved on to something else. Engaging and motivating staff Think of your employees as internal customers. Engage with them as you would any customer, and make your employee a partner in your vision. Now, it’s easier than ever to measure, improve, and boost employee satisfaction using available data and analytics options. Making learning and development more effective Learning has become a highly personal, adaptive tool offering course selections. Because online courses are so prevalent, it’s much easier for an employee to learn a new skill without time and expense away from the office. The digital transformation of this space shows how data can be used in corporate learning and professional development opportunities. This is where you’ll want to focus some of your energy should you need to upskill or reskill your employees to keep up with demand. 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.
05. June 2019
We are human. We are digital. We are both. The digital mindset and digital transformation, once heavily focused in marketing, advertising, finance, and retail also drives advances in Life Sciences. Computational Biology, Bioinformatics, and statistics. If you’re going to solve biological problems with data, you need Biostatistics. Just like you need a Data Engineer to create the parameters from which to build the structure of your Data, you need a Biostatistician to lay the groundwork to study the life in Life Sciences. This information can be infused in a variety of industries, not the least of which is medicine. We haven’t reached immortality yet, but we’re well on our way. Route to the Role of Biostatistician If numbers at the pixel level are your cup of tea, then this role was made for you. At its core, Biostatistics is the application of statistics to range of topics in biology. It is for the numbers geek with a creative streak, and encompasses the design of biological elements; the gathering and analyzing Data from experiments and offering solutions to problems in medicine, health, and many more. The educational component of this role is more often not at the PhD level and, as pharma works to beat the back the opioid crisis, Biostatisticians are on the rise. Not the least of which to reach out is the Food and Drug Administration (FDA), who have turned to scientists at UNC to fill knowledge gaps. Pharma may be in the news, but Biostatistics go well beyond this single focus in areas such as genetics, potential open source biological databases, and digital transformation throughout the medical fields. Want to know what else is in store for the Life Sciences? Trends to Watch The 2019 Global Life Sciences Outlook offers deeper insight into the following trends and offers a glimpse into the next wave of digital transformation with a focus on Biostatistics, Bioinformatics, and Computational Biology endeavors. Move over pharma legacy culture. There are new players in town. From tech giants diversifying into health care to small business startups controlling assets through its lifecycle, the next generation is shaking things up. The hunt for next gen meds has begun in answer to declining R&D returns making the case for strategic deal making a key innovation source for companies. Connection and integration of medical devices into existing care pathways across the Internet of Medical Things (IoMT) ecoysystem. Outsiders become insiders as increasing security risks spur companies to safeguard their data. Outsourcing expertise in AI, cognitive automation, and cloud computing for peace of mind. Cross-pollination of transformative technologies – physical, digital, and biological – to help forward thinking pharma companies evolve from pilots to determining how new technologies can best add value using:Artificial Intelligence (AI)BlockchainDIY diagnostics and virtual careInternet of Medical Things (IoMT)Software-as-a-Medical-Device (SaMD) Though only about twenty percent of organizations feel good about their place in the digital world, many remain in the experimental stage. Agile companies and the early adopters of digital technologies and platforms could benefit from deeper insights from clinical trials, better patient engagement, and faster life cycle times for products. A digital-first attitude will be a key driver of major change in the digital transformation in Life Sciences. Organizations will work toward a two-fold endeavor of divining how disruptive technologies can work together to provide value and meaningful transformation as well as putting humans back in the loop through training, retraining, or upskilling; rearranging the organization; and reconstructing how work gets done. Humans meet AI meet Machine Learning meet humans. If you’re interested in Biostatistics, Bioinformatics, Computational Biology and Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies for additional opportunities 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.
21. February 2019
The Eagles just took down a Super Bowl dynasty, the New England Patriots. They did it with a quarterback only three games in before the Bowl. In 2002, the Oakland A's in a bid for the Championship won 20 consecutive games with, as quoted in the movie Moneyball, "an island of misfit toys." Football, baseball, basketball, whatever the sport, New York is the home for league headquarters. The New York Metro area is one of only two cities in the country to boast eleven sports teams in the five most important professional leagues. Here, numbers, stats, and algorithms are just as important as plays, training, and wins. Okay, so wins are important. But, how do you get there? How do you know your play will hit a home run or make a touch down? It's in the numbers, the data. Even fan engagement can offer insights across social media channels - every channel, fuel to the win, plus of course great players, well-trained, who go for the win. Sports Get Social (Media)Sports have always been social; a place for friends and family to gather and cheer on their favorite team. In the age of digital, sports teams and their players join the social media landscape to better engage with fans and businesses are paying attention. Social media value in sports translates to the transfer of power of popular players and teams to products. Something like the athlete on a cereal box, but on a much wider scale. For customized up-to-the minute dashboard analysis, companies spend anywhere from $50,000 to $100,000 to determine their social media measurements. Comparing their mentions, fan reach, engagement and so on to see how they measure up against other teams and players within a specific location or sport. As social media continues to preoccupy marketing and advertising executives in sports, decision-makers are digging deeper. They want to know what's working, what isn't, what to focus on, who's being reached, and how effective their efforts are. Data is weighted depending on the sport, but across the board social media is quickly becoming one of the most important ways to speak to sports fans. Sports Marketing Insights Though TV, radio, and in the seats are still valuable and viable, the sheer magnitude of social media fandom is on the rise. Wherever you are, you can check your favorite teams scores, find player stats, and compare notes with others around the world. The key selling point for marketers? Instant feedback. Marketers now have instant access to what's working and what isn't when it comes to social media. They'll know immediately whether a brand campaign is successful or not and can gauge its effectiveness via comments and critiques. Insights via these channels allow businesses to see at once what might take them hours or days to compile via polls, surveys, or focus groups. Fan engagement insights are invaluable to not only launch follow-up campaigns, but to ultimately have user-generated content to spur engagement.In play, a team's success is measured on the scoreboard, but in social media channels, teams, players, and brand successes are measured with fan engagement. Though not everything can be controlled, we would be remiss in not mentioning analysis of pain points and injury reduction through machine analysis. Pain Points While ultimately, coaches decide who is at risk for injury and must make the determination to keep them on or take them off the field, they must first understand the computer-generated analysis. As seasons progress, fluctuations in a player's fitness and appetite for risk determine the probability of injuries.This type of analysis requires a level of computation only machines can process. But, if coaches don't understand what they're reading, they can't make informed decisions. They need to know such variables as how the predictions and analysis were calculated as well as the exact effects of analytics on performance. They need to trust the numbers generated by the machine, then combine that with their domain knowledge and experience to make their decisions. Predictive injury analysis, fan engagement, brand awareness, and sports business executives can all benefit from insights gained through analytics. Marketing insights help to propel successful campaigns into the limelight and this knowledge helps to feed everyone's bottom line. Join the ClubMultiple revenue streams such as match day, broadcasting, commercial, and now digital are driving sports teams' executives and clubs to a healthy bottom line. Fan data segmented by any number of factors include age, location, and gender while store sales and season ticket holders can be monitored to understand where and when sales are being made. Clubs use this information in their marketing campaigns to decide the best place for their stores based on demographics and fan behavior. By analyzing these results they can even boost ticket sales based on past results to balance profit margins through predictive analytics. The potential for analytics in sports has been utilized prodigiously in every sport from baseball to football to basketball and everything in between, yet its benefits are still up for debate. Data-driven insights offer a glimpse into the inner workings of teams as one unit, can help prevent injuries, as well as help individual players improve their game. When the dynamics come together the return on investment can be huge. Though the data must be translated for humans to understand, most sports clubs recognize data can create insights humans can't replicate. Analytics may offer immediate insight once played, but investment in analytics is a long game. Affecting everything from player recruitment to injury prevention to business decisions, for some the debate of its benefits remain. Yet, as the jocks claim the money and fame, the back-office nerds are developing algorithms to help businesses determine everything from player picks to ticket prices. Want to join our club? We specialize in digital and analytics recruitment and currently have an opening in our roster for a Lead Marketing Scientist with a data-rich sports media firm in New York. For a list of all our current vacancies, check us out here. For the East Coast team please call 212-796-6070, or email email@example.com. For the West Coast team call 415-614-4999 or email firstname.lastname@example.org.
13. February 2018