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San Mateo, California
A team in the Bay Area is looking for an experienced computational biologist to come in and help develop the next revolution in imaging.
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If you’ve applied for a credit card or loan recently, you’ll be aware of the swift response you now receive. No human can crunch the numbers and make the determination that fast, right? Although big banks are now adopting Big Data, Machine Learning, and AI into their legacy processes, startups have been disrupting the sector for a few years now. As banks and credit unions scramble to keep up, Fintech innovation is bringing together machine language, analytics, and AI to help form Big Data decisions in the industry. The forward-thinking potential of these technologies has led to some real-world uses to combat fraud, offer access to alternative data sources, and suggest real-time analysis for risk. So, Robots are Determining My Credit Risk? Well, yes and no. Often, those in the financial sector are using AI to assess Credit Risk. What once required Risk Analysts to determine manually, is now done in a matter of seconds with an early warning system developed by ING, PwC, and Google. This AI-powered system helps analysts make faster and more informed decisions about potential risk. How do they do this? Using pre-set criteria, they can gauge and analyze risk based on parameters such as whether or not a client has negative media coverage or if a share price falls below a certain percentage. If the world today is based on perception, even such items as bad reviews, negative coverage, and lower than average share prices can affect determinates. In addition, having these parameters can also help determine best practices and how businesses and individuals can be given opportunities outside the scope of big bank processes. However, as data breaches continue to mar profiles of both individuals and business, Machine Learning components offer platforms the chance to stem the tide of negativity. How Machine Learning Helps Prevent Fraud This is a simple process which requires two key measures. The first is to feed the machine not just a large amount of data, but knowing the parameters set, so the machine is fed relevant information. The second is human input which gives the machine its parameters to operate by. From there, the software will take the information, gain an understanding of the data patterns, and identify any signs of fraud. If done well, the automation process will employ solutions without sacrificing quality. Machine Learning in Determining Scorecard Models Alternative data sources offer more options not only to banks and credit unions, but also to borrowers. Using Machine Learning creates a more flexible, robust model when it comes to the type of information most useful to various borrower profiles. Having profiles prepared allows for automated scorecard updates and can generate better responsiveness and intelligence of a borrower’s risk profile. This process can be empowering for both startup and big bank tech. The Matured State of Analytics Though humans must initially input parameters, the benefits of Machine Learning using a decision engine can dig deeper and reach farther than ever before. This type of platform can gather a variety of scenarios across the industry and can constantly analyze the information, helping inform the processes of setting credit limits, loan origination, and risk-based pricing. As an extension of a modern analytics platform, these processes fill in the gaps where other platforms may lack the data or programming required to run effectively. But, as these platforms mature, they are helping to drive innovation throughout the Fintech industry and shaking up the outdated, cumbersome processes of old for a much more streamlined efficient operation. Want to inform decisioning and work with data engineers to build validation frameworks? Are you looking to get in on the ground floor of a startup opportunity in the Fintech industry? If so, we may have a role for you. If you’d like to learn more, check out our current vacancies or contact one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.
by Sara Cornwall
21. March 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 sanfraninfo@harnham.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.
by Riley Hsia
21. February 2019
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