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Meet Ashley Holmes. Senior Data Scientist for a firm working to improve healthcare. Or rather, the healthcare system. It’s been an unusual year by all accounts. Most jobs have moved online for the foreseeable future, yet jobless rates climb. Everyone is learning to pivot and accelerating their focus and skillsets. It’s also a time to evaluate where you are in your career and where you want to go. So, from time to time, we find it’s best to hear some stories directly from those in the field. Ashley's story begins with a desire to become a math teacher which in later years included Computer Science classes. A girl with a talent for math taking computer classes? This is her story: What drew you to Data Science from your original education focus? I’d wanted to be a middle or high school math teacher since I was 12 years old. In college, I discovered part of the math major required students to take one computer science course. I took the computer course my first semester of college, and really liked it. Programming was fun! So, to my Math major, I added a Computer Science minor in which I was the only woman. I recall a course in Operations Research in which we’d used mathematics to answer problems in healthcare by using linear algebra to optimize a design for a staffing schedule. This staffing schedule would be used by surgeons for operating rooms. Who knew there was a field where you could solve healthcare problems with math and Data? Once I knew, I dug in. Enter Binghamton University’s Systems Science and Industrial Engineering Department. Though at the time, Master’s Degrees in Data Science didn’t exist yet. But this program at Binghamton had a concentration for healthcare systems. This concentration had it all – courses for Data Science skills like Statistics, Machine Learning, and Artificial Intelligence. After some of my own horrifying interactions with the healthcare system in the US, and realizing I could use my skills in Math and Computer Science to improve it, then that’s what I wanted to do. With a graduate research assistantship from The Watson Institute for Systems Excellence (WISE) at Binghamton University, I found myself in the process engineering department at a large care management organization in New York City. It was there I got some real-world experience using clinical Data collected by the hospital to improve processes and solve problems the company had been facing. I was hooked and so I pivoted from Math Teacher to Data Scientist. It's been 10 years since you started on this path, it seems, what changes have you seen in women in the field and/or STEM focus of young women still in school? While R and Python are taught a lot more in required courses, there was no such thing as a Data Science Masters Degree when I was in school. Most of the Data Scientists I know have Mathematics, Computer Science, or Engineering degrees. Though we did some light coding in my grad school courses, most of my real programming skills have come from my graduate research assistantship and various jobs I’ve had. Talk about on the job training! When it comes to women in the field, that has grown significantly thanks to hackathons, events, and groups tailored to encourage women to enter the field. What Do You Think Now? In 2018, I heard about a non-profit hackathon in Boston called TechTogether whose mission was to end the gender gap in technology, which I thought was amazing. I’m also now part of a few professional groups for women in STEM that meetup in person and have conferences (pre-COVID) or at least have Slack channels. These advances for women in technology have been great, but there is still a lot of work to be done. I actually attended a talk yesterday by Melinda Gates (who was herself a computer science major) about how the pandemic is affecting women and girls, who mentioned that in the late 80’s when she was in school, women made up about 35% of computer science majors, whereas now in 2020 it’s down to 20%. Wait, it's Declined? Why is it Do You Think? I was curious about this too. So, I did some digging to try and find data on this, and came across this NPR article which suggests that the share of women in computer science started falling at roughly the same moment when personal computers started showing up in US homes in significant numbers. It was at this time, computers in homes were mostly for gaming, and "computers are for boys" became a popular narrative. A 1990 study shows that families became more likely to buy computers for boys than for girls, even when their girls were really interested in computers. As those kids got to college, computer science professors were increasingly men, and increasingly assumed that their students had grown up playing with computers at home. Surprisingly, this extended even to the 2010s, because I only had one female professor in my computer science department; the rest were male. Not that they were bad professors by any means, but it seemed to me even then that it was much more difficult for women to break into the profession and actually succeed. Needless to say, I was shocked (and thrilled!) when I first read the book Hidden Figures, and found out about NASA's women computers who were essential to putting human beings on the moon. I think more stories like this have come out since I was in school...I also remember hearing that Edie Windsor, who was already a hero of mine for her LGBTQ rights activism, was a technology manager at IBM. As these stories have continued to come out, I think more women have been able to see themselves as able to do these kinds of jobs, and that is part of the reason we are on the rebound. Though 2020 has been an unusual year by all accounts, it is also the beginning of a decade. What do you see for the future of women in data science and what has your experience been? With the prominence of social media now, I think it’s becoming much easier to find women in your field to connect with and ask for advice and support, and I think this is true for both young girls potentially interested in data career paths and professionals already in the industry. What steps would you recommend to young professionals entering the data professional path or those looking to change careers? Any job or networking trade secrets you wish you'd known before finding your current position? Being part of a community and making connections with other women in the field has been very helpful both personally and professionally. Join a club: Girls Who CodeGirlstartSociety of Women EngineersCheck out conferences like Grace Hopper and Women Impact Tech. Just knowing that there are women out there with jobs that you’ve never heard of can be really beneficial to believing that you can do it yourself. Look at people with the job titles you’re interested in, and see what they’ve done in the past as far as jobs, education, etc. Network and establish relationships with other women in your field. This is a very valuable tool both for getting a job and for general professional support. Take every opportunity to network that you can; I’ve gotten most of my jobs through networking and knowing people. As a Senior Data Scientist and a woman what challenges do women still face in the industry and what's something surprising you've encountered that helped you grow either personally or professionally? I think women still face a lot of challenges in the industry. Firstly, there are just so few of us. In most of my jobs (except for my current one), Data Science teams are largely made up of men. Document your accomplishments throughout your job and bring it with you when it’s time to talk promotions and raises. It is absolutely crucial to be able to speak up for yourself and be your own biggest cheerleader. I used to think that the way to advance through a career was just doing excellent work and waiting for someone to notice you and give you a raise or a promotion. I’ve found that isn’t true at all, and if you aren’t talking about your own accomplishments, who else is going to? In that same vein, finding mentors, coaches, and sponsors is critical. Finding someone who has seen your work and can speak about it and you to other people is incredibly important. Your Best Advice? My best advice is to apply for the job, even if you don’t think you’re 100% qualified. If you’re looking for a role in Data Science, Harnham may have a job for you. Check out our current opportunities or get in touch with one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.
by Alyssa Liew
03. December 2020
Jacob Glanville features in the new Netflix series ‘Pandemic’, discussing the pioneering progress that he and his team at Distributed Bio have been making in the world of bioengineered medicine. This week we sat down with Jacob Glanville, CEO of Distributed Bio, field leaders in advanced computational immunoengineering of biomedicines. Featuring in the new Netflix series ‘Pandemic’, a look into the teams that are fighting to prevent a global outbreak of disease, Glanville is a highly renowned expert with an incredible track record. With a PhD from Stanford, and having spent four years as a Principal Scientist at Pfizer, he left to found Distributed Bio. With Sarah Ives, Director of Influenza Centivax at Distributed Bio, the team is developing a new class of universal, utilizing pioneering computational technologies. “We use high throughput computational docking to try to help characterize how many unique epitopes might exist on the surface of a viral coat protein or a pathogen protein. Then, we also use computational methods to identify distinct elements of those diverse members of viral cost proteins from lots of different evolved versions of the same pathogen. And that's the centerpiece of how our vaccine technology works. We co-administer a bunch of really different variants all at a low dose so that only the shared sites are essentially at a high enough dose to be responded to.” This technique allows for Distributed Bio to create vaccines for almost any virus, at a fast pace, and in a safe environment. For example, with the recent outbreak of the SARS-derivative Coronavirus, Glanville is working in collaboration with US military and World Health Organization’s program allows the creation of ‘pseudo-virion’ versions of the disease that can be examined without posing a significant risk: “They take chicken pox, and flow over the outside of the chicken pox, the cost protein of a more serious virus, like the Coronavirus. So it behaves like a Coronavirus and it looks like one on the outside. Like the crunchy M&M shell is, is Coronavirus, but it's got the soft gooey M&M chocolate of, of chickenpox. It's not that dangerous. We are setting up a relationship with [the military] where we could use our antibody discovery library in conjunction with their pseudo-virion particles. We could rapidly discover antibodies against, SARS for instance, without the risk of bringing SARS into our lab.” Their work, however, is not just limited to fighting viral diseases. One of Distributed Bio’s leading projects focuses on creating a universal antivenom to snake bites. With between 80,000 and 130,000 people killed each year by snake bites, the majority of whom live in third-world countries, the need for an easy access and affordable antivenom is high. “There's around 550 snakes in the world and each one has 20 to 70 proteins. It seems like a huge number of proteins you'd have to target to hit all snakes. But, for me analyzing them, they all collapse down to like 10 different clusters and homologous groups that all snakes share.” Having discovered that a universal approach was both possible and realistic, how did they develop the antibodies needed? “Our team [led by Tim Friede, Director of Herpetology at Distributed Bio, Sawsan Youssef, Chief Science Officer, and Raymond Newland, Principal Scientist.] found a man who spent 17 years injecting himself with snake venom from all over the world, because he loves snakes, and we took his blood. We’ve been using lab methods plus computational methods to help identify a series of antibodies that can hit like a bunch of shared determinants.” But, with a team that comprises of roles varying from Data Engineers and Data Scientists to Bioinformatics specialists, the ability to work together is essential. How does Glanville look to create a collaborative environment? “I actually try to cross-train people as much as possible. My feeling is, that the extent to which you can actually cross-train people, the less likely you are to encounter a series of like assumption errors. I think what happens is often down to miscommunication between people who are making errors in the cracks where they have both misunderstood what the other person needed and what the previous person was giving them. If people are able to take their colleagues’ expertise into question when they’re working, you've reduced some of that risk.” Having grown up in Guatemala, Glanville is all too aware of the need for easily-available and effective vaccines, particularly as the Western world grows more wary of injections, largely due to the amount of misinformation that is currently circulating. But he understands that these concerns are often down to trust: “It's hard to communicate an epidemiological recommendation to a global population and not make it one sentence. And so, the loudest sentence becomes ‘get no shots’. I'm hoping that a more effective shot makes the story go away. The problem currently with a flu shot is that it still only works half the time. And so people complain about it. I’m hoping that better vaccines and more reasonable communication will cause calmer minds to prevail.” As for any immediate concerns about the impact of the Coronavirus, he once again turns to the issues of accessibility: “Right now I worry more about Ebola. It's a larger outbreak problem and it's in an area that is poorly served. I think China is pretty good at locking down medical problems.” If you’re looking to build out your team with the industry’s best, get in touch with some of our expert consultants: 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. If you’re on the hunt for your next opportunity and want to join an innovative, world-leading company, we may have a role for you. You can find our latest jobs here. Pandemic is streaming on Netflix now. You can watch the trailer below.
30. January 2020
It’s open enrolment for healthcare here in the US with a maze of plans to choose from. If you want to dip your toes into the world of healthcare with a tech bent, you may want to check out Bioinformatics or health informatics, and yes, there is a difference. Bioinformatics is a growing field and is expected to grow to $16 billion by the 2022. It may just be the next “rock star” profession for those in the Data & Analytics fields. So, what is Bioinformatics and how is it different from Health Informatics? What is Bioinformatics? It’s the marriage of biology and information technology. In a world constantly on the go, and as we grow older and live longer, it helps us find the answers we seek. Bioinformatics often begins at the beginning. Think genome research, for a start. Yet, ultimately, it focuses on biological data in medical research and drug development. Imagine collecting and organizing data to annotate, record, analyze, and extract structural information in relation to protein sequences or applying your knowledge to chromosome therapy, drug innovations, or forensic analysis. Because of the advances in IT, what was once unimaginable is now available. A booming industry which is a boon to the population. House, M.D. meets Bones. Within this industry are sub-categories and sub-applications. In other words, there’s something for everyone interested in both biology and computer science. Here’s a quick list: Medical BiotechnologyAnimal BiotechnologyAcademicsAgricultureForensicsEnvironmental And within these sectors, though not the full list, their applications: GenomicsChemoinformaticsDrug designTransciptomics What is Health Informatics? Health Informatics is similar to Bioinformatics in that it uses computer technology to further advancements in medicine. However, while Bioinformatics focuses on the biology side of things, Health Informatics (HI) is focused on the patient side; helping doctors and patients determine care. HI is the application of design, development, and analysis of patient and healthcare Data systems. It’s the nervous system equivalent of a hospital or doctor’s office which houses medical records, billing systems, and compliance systems. For those with a computer science background who are more interested in the information infrastructure and architecture of a healthcare enterprise, Health Informatics may be for you. If you’re interested in the administration side of healthcare, you may want to think about Health Information Management (HIM). You can also learn more, here. Getting Your Foot in the Door You know the basics. Have a technical background with the communication skills to explain your findings. Boost your resume with video. Have done a project or two to show your work and capabilities, but when you drill down to something like informatics, there’s one more bit of training you’ll want to have. Since Bioinformatics, for example, is the marriage of biology and technology, it’s important to have a background in molecular biology and computer science. Drill down further and you’ll want to include database design as well. The Sum of its Parts Bioinformatics is an emerging science, in which we develop and use computer databases to enhance our biological research. Analyzing, storing, managing the data we collect or extract; this is the sum of its parts. Advancements here give us the opportunity to more efficiently identify new therapies, new treatments, new sequences to better understand disease. The potential to improve personalized medicine is exponential. What we learn and find today may help us solve tomorrow’s healthcare issues. Want to get in on this growing healthcare field and the next generation of IT? Interested in Big Data and Analytics, but not necessarily the healthcare industry. We’ve got you covered. We specialize in Junior and Senior roles. We 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, 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.
07. November 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 RNA 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 sanfraninfo@harnham.com. For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.
04. July 2019
Boston, Massachusetts is once again on the cutting edge of medical research and technology. From Electronic Health Records (EHR) to Machine Learning and predictive modeling of healthcare best practices to Computational Biology; the final frontier of genetic editing. We have come a long way in our quest to understand and improve our quality of life. In the face of cancer research, diabetes, and liver or heart failure, the world of Computational Biology opens the scientific doors to discovery and solution. This is a place for scientists to not only get to the heart of the matter, but to the core of the problem at the cellular level. There is an old adage which states, “when pigs fly”, usually meaning some thing will never happen or is impossible. But what happens when the impossible becomes possible? The jury’s still out, but researchers are making great inroads in developing ways to save human lives using animal organs. Could Animal Organs Help Solve Donor Deficiency? There are over 100,000 patients in the U.S. waiting for a transplant operation and, for many, a this may be their only cure. Yet, our growing population and the sheer number of those waiting has created a donor deficiency of epic proportions. Researchers have been working toward successfully transplanting organs from animals into humans. Not only has their study of stem cell technology grown over the years, but with the advent of bioinformatics, statistics, and Computational Biology, a new possibility has arisen. The chance to not only transplant organs from one species to another, but using another species to host the growing of transplantable human tissue. Getting the Framework Right Computational Biology is a broad discipline honed to a fine point. Using statistical modelling, it builds a wide variety of experimental Data and biological systems to understand algorithmics, Machine Learning, automation, and robotics. Its job is to ask and answer the question of how to efficiently gather, collate, annotate, search for information. But how can it do all this to determine appropriate biological measurements and observations? At the tipping point is the notion that to truly get a good picture of the problem, the frame must be in focus. And it is this, which is the most important task for Computational Biologists to solve before continuing their research. It’s a reminder to step back and look at the problem from another angle and to challenge assumptions turning “what if” on its head. Stretching, bending, and twisting toward a solution that might not otherwise have been thought without a framework in place in order to begin modelling the system. It is in this constant learning phase, Machine Learning applications with parameters set by the biologists, in which new information is processed, analyzed, and understood. This active learning model offers opportunities for applications to learn how to learn and will play a critical role in biomedical research now and in the future. And from this place, the second biggest problem to be solved enters the equation. Now, it’s time to refine the methods of how to solve the problem. Next Steps As exciting as the possibilities are, like all things new, there are challenges. For example, not all animals will fit the bill for transplantation. The idea is to mimic as closely as possible the size and evolution of humans such as pig, sheep, or non-human primates. But, at an even finer point of challenge are our own cell’s reactions and expressions and understanding why they act the way they do. Ultimately, it’s important to be sure information at the individual cell level is inferred with statistical references to verify findings. At the pixel level, not using a fine-tooth comb could mean your conclusions are wrong. If you’re interested in Biostatistics, Bioinformatics, Computational Biology, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our latest Computational Biology opportunities in our new Life Science Analytics specialism or our current vacancies for additional opportunities. 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.
13. February 2019
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