Undergraduates who are looking for a career that will positively impact human health are likely aware of two general career paths: basic science researcher, and health care professional. Basic science researchers help improve the understanding of disease and participate in the initial discovery stages of treatment development, while health care professionals are doctors, nurses, physicians assistants, or others who actually administer the treatments to patients. However, there’s a lot of work that goes into developing the potential discoveries of research scientists into approved and effective treatments that can be administered in hospitals. Rigorous clinical trials are a crucial bridge connecting basic research to actual real world human health outcomes. To get some insight on what it’s like to have a career in this important bench-to-bedside bridge area, I spoke with Emily Stone, who has been working in clinical trial management since her graduation from University of Rochester in 2009. Emily earned her BS from U of R in Brain and Cognitive Science, and earned her MBA from Babson in May 2017. She is currently a clinical trial manager at a small startup in Waltham, Massachusetts.
Undergraduate Studies and Work Experience
Emily initially started as an undergraduate at the University of Rochester majoring in Biomedical Engineering (BME). After about 18 months, she realized that it wasn’t the right field for her and made the switch to Brain and Cognitive Science (BCS). “I was just not happy, wasn’t having fun [with BME]”, she says, and ultimately chose to major in BCS because while she wanted to stay in science, she explains that she found the ‘granular’, detail-focused nature of BME to be a bit dry. She felt BCS was the most interesting science and she knew it would give her a lot of options after graduation. “BCS can be artificial intelligence, brain surgery, marketing, a lot of options,” she says. In the end, it turned out that a job she had as an undergraduate really prepared her for the career she has continued in since graduating. Emily was hired as a Research Assistant at Strong Memorial Hospital, which is associated with the University of Rochester, working on autism and down syndrome studies in the pediatric department. “I found that job because I started applying for anything I thought I was remotely qualified for. Even if I didn’t meet all the requirements, but some of them, I applied, because you don’t get what you don’t apply for,” she says. While the work wasn’t exactly brain and cognitive science, it was her first experience working in clinical trials and it turned out to be extremely valuable experience that helped her find a job after graduating. “I was doing data entry and learning about what clinical research is,” she says of her time at Strong Memorial Hospital. Thanks to that experience, when Emily graduated she already had a skillset that made her qualified for positions working in clinical trials that she was interested in.
Thoughts on Artificial Intelligence
I asked Emily to elaborate on how a BCS major could help with a career in Robotics. University of Rochester actually offers a specialization in Artificial Intelligence within the Computer Science major offered at Emily’s school. On the University of Rochester website, studies in this specialization include “work on knowledge representation and reasoning, machine learning, dialog systems, statistical natural language processing, automated planning, AI-based assistive technology, and computer vision”. Although she did not choose that major, she did take programming classes and thinks the field of AI is very exciting. “At the time I didn’t understand the breadth of what AI could do, all of its capabilities,” she says. “I think one of the biggest takeaways is that we know a pretty good amount about the human body but when you get to the brain, we only know a very small percentage about how the brain functions… There are so many things we could learn about how our own brains work and how we think and process information, because we make split second decisions that are easy for us but difficult to communicate in a programming language. How to teach a machine to tell the difference between a small child and a rolling ball, for example”, she explains. Therefore, having background in neuroscience, some understanding of how human brains work, “could inspire how we program machines”.
Career Progression in Clinical Trials Management
Her first job after college was in the TIMI group (Thrombolysis in Myocardial Infarction) at Brigham and Women’s hospital which she describes as “an academic research organization for cardiology clinical trials.” TIMI has a network of physicians around the country and they work with sponsors, usually large pharmaceutical companies, to help set up clinical trial sites up and keep them running. “That job gave me a foundation of knowledge about what it takes to start up a clinical trial, to get a site going,” says Emily. During her time as part of the TIMI group, she was involved in ensuring sites had all the documentation they needed and what guidelines needed to be followed– basic regulations that are fairly common across all clinical trial work.
After about a year in the TIMI group, she moved to a different position within Brigham and Women’s Hospital at the Center for Clinical Investigation. This new group was an all purpose group of in-house clinical program managers. In this new position, there was a lot of variety in the work. “Instead of working in one department, we were working across multiple departments”, she says. “Physicians could say I need help screening patients for a trial, or I need help submitting IRB paperwork, or collecting drug samples, or collecting mitral valve tissue during open-heart surgery– Anything they needed, we would do it”.
She was also able to attend training courses on good clinical practices, how to properly conduct studies, draw blood, build electronic database capture systems, and more. The broad exposure made the work fun and she was happy with the position for a while. Eventually though, it was time to move on; “I just didn’t feel like there was a career track from research coordinator”, said Emily. She explained that many people in the role are in an in-between area career wise – whether they are waiting to get into medical school or MPH (Masters in Public Health) programs, or just getting ready to make a shift career wise — and that the group had a lot of turnover. Although she knew she wanted to go back to school, she wasn’t sure what she wanted to study yet, so she decided instead to make the transition to industry.
Emily soon found a role at a small company through a contact she made at a career fair. “That was a great opportunity– it was a good company to work for, had good business practices, the people were really welcoming and that’s where I learned what it meant to work for a sponsor of the clinical trial, to provide oversight of the trial, hire vendors, how to make sure the trial is ready for regulatory agency inspection, how to get new drug approved”, she says. In this role, she had the rare opportunity to follow a single drug all the way through the development process and see it approved for use by the FDA. “When I started I was supporting four global phase 3 programs [large multinational clinical trials]. Those programs were 50% of the way through startup when I joined, so I got to see them through startup, enrollment, closeout, and there was even a new drug application (NDA) for the compound. So I saw the product all the way through FDA and European agency inspections. It’s unusual to see a drug all the way through trial, to inspection and approval… It was a really neat experience”, she says. The success of the company led to its acquisition by a large pharmaceutical company– at which point Emily decided it was time to move on again.
This time, the company was not as successful. She moved to a small oncology company that had only one drug candidate in clinical trials, and the drug did not work as expected. “There was no more funding and I got laid off,” she says. She then moved to another small oncology company, and a similar situation happened. They were not able to get adequate data to prove efficacy for their drug candidates, funding dried up, and she was laid off. After those experiences, she moved away from the oncology space and is now working at a startup that develops drugs to treat infections. “I’m managing multiple trials and overseeing a much wider scope of activity”, she says.
While it can be intimidating to change jobs, Emily took calculated risks with moving to new companies when she felt the time was right, and has been able to work in clinical trial management positions with an increasing amount of responsibility and compensation. Through this progression she was also able to gain experience in all aspects of clinical trial management, which has helped her become knowledgeable about all the steps involved and put them into the overall context of moving a drug through a trial, inspection, and approval. “As I’ve progressed in my career I’ve learned about different aspects of running a trial… And I’ve been able to develop that knowledge. Now, where I’m the one making decisions, I think more about, ‘how do I select the right vendors and get financed? And how does this fit into the overall strategy of the company?’”, she explains.
The challenge with drug development is that many drugs don’t work and don’t make it all the way through to FDA approval, and funding is often contingent upon the success of the drugs. As Emily experienced, this type of work can be unstable and it’s not uncommon for companies to lay off entire groups, or large percentages of their workers after a candidate fails. She states the work can also be tedious because everything needs to be documented properly, and if it’s not documented, “it’s like it didn’t happen”.
Like all jobs, clinical trial management has its problems, but there are plenty of upsides to this career. Emily’s favorite thing about the field is that she feels there is a great opportunity to have an impact on a patient’s life. “We have new products and new technology and are always addressing an unmet medical need. I also like that there are different challenges every day, which is exciting”.
As for what it takes to excel in this career path, she cites organization skills, an outgoing personality, good general managerial skills, and an ability to balance attention to detail with big picture perspective. Emily explains, “If you get too bogged down in details, you’ll make a process so complex that no one can follow or comply with it, it’s too specific. It’s important to keep a balance, be logical, and to not forget about the operational aspect… I always try to pretend that I am at a site reading the manual and that I have to follow it – do I know what to do? Who to contact if I have trouble?”
Emily has had a fruitful career so far helping clinical trials programs function and serve their purpose as a bridge from bench research to bedside medicine. This is an area with a lot of job opportunities as research continues to progress and new drugs are developed, and it’s worth considering as a potential option for new graduates of the basic sciences.
What is Machine Learning?
For those wondering what machine learning is, its name is quite self-explanatory: a machine that learns. Machine Learning is not a new discovery; It has been known to exist since the 1950s. It stemmed from the grounds of artificial intelligence alongside the interpretation that our brains are essentially computers. In Gary Donovan’s article, “7 Machine Learning Pioneers Timeline,” he writes about Arthur Samuel, one of the early pioneers of machine learning who created the first program where the machine’s purpose was to learn and improve on its own. This was exhibited by programming a machine to play checkers. As the computer progressed throughout the game, it managed to discern good and bad moves, and improved by broadening its search tree (Donovan, 2015). The program allowed the computer to learn from experience, as humans would. This groundbreaking creation defied the ideology that programs are instructions meant to be highly specific, rigid, and inflexible for a machine to perform a particular task successfully. By allowing a computer to learn from progress, it was evident that it was possible to program a machine to a certain extent and have it figure out the rest of its tasks by itself, unsupervised.
The Identity of Machine Learning and Tips for Starters
Since machine learning is like a condensed variety of important topics, it’s essential for one to know what to learn and how to get started. The best and well-known fields that could give a student a step ahead in machine learning occupations are particularly Data Science or Computer Science. Machine learning’s entity is a combination of both. Another important area of machine learning is the realm of “deep learning,” which, as seen in the Venn diagram below, is a subset of machine learning that is concerned with algorithms that imitate neural activity (also generally known as neural networks.) Although there exist debates in which Venn Diagram is best to visually represent the differences of the fields, the Data Science Venn diagram by George Piatetsky-Shapiro (President of KDNuggets, the leading website that focuses on Data Science, Data Mining, Machine Learning, Analytics, and Big Data), is used for reference:
In the statistical aspect of things, this career path involves plentiful data, and with better technology at present time, huge amounts of data are stored on a daily basis. Specifically, in the realm of “Big Data” (a term used to describe a huge volume of data to be used for analysis (SAS, n.d.)) machine learning is involved with Data Science to analyze these huge collections of information. Data mining particularly uses machine learning to apply to Big Data (IO Technologies Inc., n.d.), and that is how machine learning partially submerges itself to the data science and the data mining world. The huge data input can then be observed for patterns, which kicks in the engineering application of pattern recognition. This general gist of what goes on in the ropes of machine learning is essential for the making of inferences from a huge sample that leads to the creation of predictions or predictive models.
Hand in hand with math and engineering, computer science is also important in machine learning. Author of the book Introduction to Machine Learning, Ethem Alpaydin (2014) states “Machine learning uses the theory of statistics in building mathematical models… The role of computer science is twofold: First, in training, we need efficient algorithms to solve the optimization problem, as well as to store and process the massive amount of data we generally have. Second, once a model is learned, its representation and algorithmic solution for inference need to be efficient as well,” (p. 3). Computer science is also connected to the very important branch of artificial intelligence in the machine learning process. since it is needed for artificial neural networks. Artificial neural networks are responsible for processing information similarly like how our brains process information (Kumar et. al, 2010). It allows for adaptation when changes occur, giving it the ability to learn without giving every solution for every possible situation (Alpaydin, 2014, p. 2).
There is more in-depth information to be shared about machine learning, but there have been a few bits highlighted for anyone who is willing to have a take on the career. In William Terdoslavich’s article “Real Skills You’ll Need for A.I. and Machine Learning,” software engineer at Infer, Joel Dodge, mentions that it is uncommon to find applicants who have years of machine learning experience, so to fix that issue, most are expected to efficiently do a lot of on-the-job learning. Abdul Razack, head of platforms and senior vice president of Infosys, also shares to Terdoslavich an alternative hiring approach of taking specialists of a different field and teaching them the skill set needed for machine learning. Additionally, head of machine learning at Bloomberg, Gary Kazantsev stresses that subjects such as probability, statistics, linear algebra, and mathematical optimization are all important for those wishing to develop their own algorithms (Terdoslavich, 2016). It is essential to remember that a degree and a school certificate is a small fraction of what the hirer weighs in his or her mind when it comes to the hiring process. The ability to showcase one’s potential in the workplace is also important. If anything, it is of bigger weight to be taken into consideration.
In a more experience-based context for machine learning, Vincent Granville’s blog post “Data Science and Machine Learning Without Mathematics” describes the not-so-math-heavy techniques of his day-to-day tasks. He begins to enumerate several helpful articles he has written which further describes his machine learning job activities on a day-to-day basis that do not require heavy math use: variance, clustering, and density estimation, model-free confidence intervals, and more. He mentions that basic excel skills helped in advanced machine learning and can aid in the implementation of important techniques involved in a machine learning career (Granville, 2017). So for anyone who wants to get ahead, mastering excel is a great place to start!
Many individuals self-learn machine learning and a lot of content online are provided to get an idea of starting. Eric Jang, a research engineer at Google Brain, claims that there’s no actual way to learning this field rather one should come up with study methods that the he or she is comfortable with. He also suggests specializing and choosing one sub-field of Deep Learning (in which he lists down) as a trick to be efficient in a specific aspect of machine learning (Quora, 2017). For people who want a more organized walkthrough for independently studying the career, sites like https://www.machinelearningmastery.com and https://www.elitedatascience.com offer step-by-step procedures, as well as other resources, such as free textbooks available for reading.
Machine Learning Applications
As mentioned previously, machine learning isn’t something people converse about casually. However, it is far common in everyday use than people realize it to be. It’s not a surprise that a person can encounter typical machine learning in a day. A familiar name that people know is “Siri,” who of which is a great example of exhibiting the applications of machine learning. Virtual personal assistants like Siri need machine learning to gather the information of how the user previously interacted with them, which in return gives the user a more refined accuracy in results based off of the user’s preferences (Daffodil Software, 2017). Using search engines such as Google’s also involves machine learning since the program watches search activity and interprets how the user interacts with the search results. From there the program learns to render better search results that will fit more accordingly to what a user is searching for. Other applications, among many, involve fraud detection, smart cars, financial trading, and even your simple and almost present recommendations from Netflix (Marr, 2016)!
The list can drag on for common machine learning encounters when it comes to business and technology, what with the main statistical and computer science requirements to fit the occupation, but believe it or not, machine learning is also excelling in the world of medicine. In the article “7 Applications of Machine Learning in Pharma and Medicine,” Fagella writes about a study that estimated a generated value of up to $100 billion annually in the pharmaceutical and medical industry with the help of machine learning. It was mentioned that the healthcare sector has been great in accumulating data from research, development, patients, caregivers, physicians, clinics, and more. This paves the way for machine learning to help analyze the collected data to aid the prevention of diseases as well as the treatment of individual patients. Enumerated were particular aspects of this industry that would work efficiently with machine learning. To name a few, disease diagnosis, drug manufacturing, drug discovery, clinical trial research, radiology, and behavioral modification are all of the areas that perform with better efficiency with machine learning. However, most of these examples are still works in progress and research and experimentation is still an ongoing task to pave way for machine learning to do its magic (Fagella, 2017).
Machine learning can also be applied to social issues. In an MIT’s Technology Review article, students from the University of Rochester managed to create an algorithm that trains the machine to detect alcohol-influenced tweets and to use this set of data to monitor alcohol activity. They filter tweets that mention alcohol or any word that might be a connotation of an alcohol-related activity. This work also has another inspiration and that is to locate the Twitter user to determine whether they are drinking at home or not. A list of filtered words that would signify if a user was in their own vicinity is again used to identify the user’s location. Nabil Hossain and his fellow co-workers who are hard at work in this project believe that this method can significantly affect how we respond to the health problems that are caused not just by alcohol, but other activities that could cause a public health issue (Emerging Technology From The Arxiv, 2016).
It is evident that machine learning is an integral part of today’s society. People who are aware of machine learning existence know that the applications that do exist and the ones that will exist in the future will be very useful alongside the technological advancements the world has yet to provide. A viable reason as to why its existence is a little minimized from common knowledge is because it is a career path that highly interconnects other fields. However, this characteristic shows that a machine learning career is diverse in terms of the subjects involved in being successful in this career path. The options of where a person wants to practice his or her machine learning expertise is plentiful. Individuals who are uncertain of which path to pursue but have spread interests in the fields of STEM might find machine learning to be a career of interest and should definitely consider looking into it!
Alpaydin, E. (2014). Introduction to Machine Learning. Cambridge: MIT Press.
Daffodil Software. (2017, July 31). 9 Applications of Machine Learning from Day-to-Day Life.
Retrieved October 27, 2017, from https://becominghuman.ai/9-applications-of-machine-learning-from-day-to-day-life-112a47a429d0
Donovan, G. (2015, May 5). 7 Machine Learning Pioneers Timeline. Retrieved October 27,
2017, from http://techjaw.com/2015/05/05/7-machine-learning-pioneers-timeline/
Emerging Technology from the arXiv. (2016, April 25). Machine-Learning Algorithm Identifies
Tweets Sent Under the Influence of Alcohol. Retrieved October 23, 2017, from https://www.technologyreview.com/s/601051/machine-learning-algorithm-identifies-tweets-sent-under-the-influence-of-alcohol/
Faggella, D. (2017, September 01). 7 Applications of Machine Learning in Pharma and
Medicine. Retrieved October 23, 2017, from
Granville, V. (2017, June 26). Data Science and Machine Learning Without Mathematics [Blog
Post]. Retrieved October 30, 2017 from https://www.datasciencecentral.com/profiles/blogs/data-science-and-machine-learning-without-mathematics
IO Technologies Incorporation (n.d.). What is The Difference Between Data Science, Data
Analysis, Big Data, Data Analytics, Data Mining and Machine Learning? Retrieved October 27, 2017, from https://onthe.io/learn/en/category/analytic/What-is-the-difference-between-Data-Science%2C-Data-Analysis%2C-Big-Data%2C-Data-Analytics%2C–Data-Mining-and-Machine-Learning%3F
Kumar, J. B., Bhattacharyya D., Kim T. (2010). Use of Artificial Neural Network In Pattern
Recognition. International Journal of Software Engineering and Its Applications, 4(2), pp. 23-34.
Marr, B. (2016, September 30). The Top 10 AI And Machine Learning Use Cases Everyone
Should Know About. Retrieved October 20, 2017, from
Mayo, M. (2016). Data Science Venn Diagram [Digital image]. Retrieved October 29, 2017,
Quora (2017, March 22). What Is The Best Way To Learn Machine Learning Without Taking
Any Online Courses? Retrieved October 25, 2017, from https://www.forbes.com/sites/quora/2017/03/22/what-is-the-best-way-to-learn-machine-learning-without-taking-any-online-courses/#599a5dbc5d87
Terdoslavich, W. (2016, November 3). Real Skills You’ll Need for Machine Learning for A.I.
and Machine Learning. Retrieved October 25, 2017, from https://insights.dice.com/2016/11/03/real-skills-ai-machine-learning/
What is Big Data and Why It Matters. (n.d.). Retrieved October 27, 2017, from
What is your major
Quantitative Economics with a minor in Neuroscience
How did you get interested in STEM
I fell in love with science after reading Stephen Hawking’s A Brief History of Time.
It blew my mind how much of the universe is still unknown.
How did you find out about iTrek
I was forwarded a recruitment email from a professor. It turned out to be a very wise decision to apply.
Which Trek do you participate in?
I was the Trek leader for the Summer 2014 Trek. The topic was coral reef health.
What have you been doing since the Trek?
Since the trek, I have worked to finish my BS and graduated in December of 2014. I am now pursuing a degree at the Wharton School Of Business.
I have used the skills taught to me on my Trek to successfully apply to other internships and continue to develop my academic resume including an internship in Georgia State University’s Experimental Economics Department.
What do enjoy doing in your spare time?
I love to get out doors and exercise. I play ice hockey and tennis during my free time. I have also been trying to learn to write code; Python is my favorite language so far.
What is something you are really looking forward to?
In the words of Peter Thiel “if you can see yourself in a place for more than two years, do you really want to be there?” I look forward to the next adventure I get myself into, regardless of what that may be.
Would you recommend iTrek?
I would definitely recommend iTrek to an undergrad student. There are very few internships that allow you to completely choose your topic and develop the procedures. I had a lot of fun and met some incredible people! I am very proud to have participated in this program.
Where do you see yourself in ten years?
I hope to work in a large bank or analytics firm. I really enjoy interpreting human behavior and using that information to make predictions about the future. I want to find a career that allows me to be dynamic and presents a new challenge every day.
Written By: Nicole Castagnozzi
For today’s undergraduates, concerns about finding steady employment after graduation are common and well warranted. With an increase in the number of people earning college degrees, white collar employment has gotten more competitive, and companies often ask for experience even for entry level positions. Further complicating the situation is our current “robotic revolution,” a technology driven innovation economy where newer, smarter technology is constantly evolving and edging out older manufacturing practices, consumer products, services, infrastructure, and operations within businesses. Students in STEM fields already have a leg up when it comes to entering into this rapidly evolving economy, because an analytical, methodical and scientific educational foundation is often required in technology based “innovation” fields. However, maintaining curiosity and creativity is important for efficient and novel engineering design and optimization. For students who are inspired by the idea of entering into the exciting, fast paced world of modern technology – and perhaps even dream of starting their own company one day – obtaining a STEM foundational education, while curating a variety of skills along the way is an ideal preparation for a successful modern career. Although it may seem counterintuitive, following a non-linear career path can also be a great way to explore passions and find the job best suited for an individual. To get an inside scoop on what it’s like to be a career-wanderer turned entrepreneur with an engineering background, I spoke with Justin Rothwell, CEO and co-founder of the startup ProAxion based in North Carolina.
ProAxion builds small adaptable devices that attach to machinery in manufacturing plants, and monitor indicators of that particular part’s health, such as vibration speed and temperature, in order to monitor and alert floor supervisors to problems before there is a catastrophic break that holds up the entire production line. This simple device can save time and money by anticipating problems before they occur. Justin, a mechanical engineer by training who holds a PE license in Massachusetts and an MBA from Worchester Polytechnic Institute, co-founded ProAxion with a friend and fellow engineer Elliot Poger in 2015. Below are some interesting points from our conversation.
Early Jobs Working as a Design Engineer: Expectations vs. Reality
“I was always fascinated with machines, kind of how things worked, I was really just taking things apart, not so good at putting them back together!” Justin says of his choice to major in Mechanical Engineering in undergrad. “Engineering became a path that interested me, and the people in my life said ‘you know that’s a good field, you can make some good money, and it’s a good career path’”. Justin was very interested in space exploration and the exciting technology NASA uses for its rockets and satellites, and he was also inspired by a family friend who worked as a design engineer for airplane turbines. With the goal of working as a design engineer, he graduated from Northeastern University with a BS in Mechanical Engineering. When he graduated, he did find work as a design engineer, as he’d planned to do, for a company that designed and produced specialty pumps. However, the experience was not what he expected. “It wasn’t very glorious, no satellites or anything,” he says.
He found that the purely academic approach to design and problem solving he learned in school was not effective with the product he was working with. “I remember being told, because I brought a bunch of textbooks from my college courses with me to the job, and the senior engineer there, who had been doing this for 30 years, said ‘yeah, no textbooks, we don’t have any textbooks here. This is the real world of engineering’. I didn’t know what to make of that”, Justin explains. “You’re taught in school, there’s a problem, you outline it, come up with a proposed solution, reference some equations, and then you get a solution, and there you go. And these machines, they were very high speed pumps, with very close tolerances between the moving parts and non moving parts, and you know they didn’t follow any type of equation.” Real world design was more about experience and resourcefulness, and Justin found the mathematical equations he relied in in undergrad had a narrow applicability. Learning to think outside the textbook was a common challenge among his peers as well, he says. “If you’re [majoring in engineering] to become a teacher, tenure track, then, it stays very much the academic approach.” but, “in engineering there is a wide spectrum from the pure academics, all the way to the other side which is field engineers, [who use] a lot of creativity, resourcefulness, throw the book out and just make it work,” he explains. Later, Justin decided to take an opportunity to work as an engineering consultant designing entire water treatment plants. “It was, to me, very fascinating to learn about how the whole plant worked,” he explains. Again, expectations still failed to line up with reality. “I was expecting, hey, we are going to design the best system we can, the best plant, you know, because it’s all public water supply. But at the end of the day it was a business and there was a lot of pressure to do things quickly and cut corners. And that created a lot of frustration,” says Justin. The lessons learned in that job, although tough and at times discouraging, spurred an interest in business and he decided to enter graduate school to earn an MBA.
On graduate school and the value working in a cohort (like i-Trek cohorts!):
Justin’s MBA program was a part-time, cohort program that was 32 months long and focused on innovation and entrepreneurship. Working together in a cohort allowed Justin to develop deep relationships with his classmates and he learned about himself and what personalities he meshes with in a working environment. “At the end of the 30 months, I was friends with everyone, but there were certain people that I worked better with,” he says. “You get a few layers deep in terms of people’s strengths and weaknesses and how everyone is different. How puzzle pieces fit together in terms of a team. And that experience although it helped us in our cohort, provided a lot of learning in a professional context, especially if you are going to work as part of a team, or grow a team.”
On the value of an engineering education and diverse working experiences:
Aside from the obvious technical expertise engineering students learn, there’s another more intangible learning that goes on. “To me, that was the biggest benefit of engineering school, learning to be a problem solver. Not memorizing formulas, or how to solve this specific problem, just generally how to be resourceful, think critically about a problem and be creative. But it wasn’t until I got into the professional world that I realized that that is a skill set you learn”, Justin explains. While many of the technical engineering skills may only be applicable to very specific professions, this resourceful, problem-solving mindset is widely applicable to a huge range of professional roles. “You become a critical thinker, it’s a good experience, that is translatable in a lot of non-technical ways,” Justin says.
Additionally, as careers develop many engineers may chose to move out of technical engineering roles. “If you apply this [education] differently, that’s ok, that’s all kind of part of the experience. If anyone tells you that they had a plan on day one, and executed it perfectly 10 years later, I mean, I think they’re full of it,” Justin says. Given his experience with his own career development, Justin advocates taking a non-linear path to find your own success. Diverse job experiences allow young professionals to find out about themselves, what they like to do, and what kind of role is a good fit for them personally. Coming out of undergrad, the focus is frequently on the formulaic technical skills. “I think switching jobs or roles gives you that kind of experience” that allows young engineers to grow and develop self awareness, so they can work better in teams and be more dynamic. “It can be scary, but I tend to advocate for that”, he says. “Get some other data points that are much different than the ones you have, so you can triangulate, and I think it’s a lot of self discovery. Like, that was an awful experience. Well that’s great, because now you know, ‘I’m never doing that again’.”
On creativity, risk taking, and making mistakes:
Sometimes neglected in the academic engineering environment is the creativity and agile adaptability that Justin has found indispensable while building his own company. To develop the product that ProAxion is now selling, he and his co-founder focused on what he calls an “80/20” approach: “Don’t spend too much time engineering the perfect solution up front,” he advises. “Get an 80/20 approach, but then repeat that rapidly. If you do enough 80/20 passes on a problem, you’ll get there much more efficiently than if you spend a lot of time trying to get to the 99 percent solution without even trying it, without the experience”. Using this approach they were able to design their product, which is now ready for commercial applications, in about a year. “The first one was just a proof of concept, we were never even trying to sell it, it was just a developer board in a small box with a mac mini in a waterproof suitcase. Just something that, what could we get done in a week? Something out there to prove that it has a business need, something with a customer would let us put in and pay us for”, he says. “Our current system is generation 4, within 18 months. And… it’s a commercial product now”. In essence, his experience has taught him that putting your designs out into the world and trying them out is an efficient way to optimize. “There’s so much learning in the mistakes, and trying to get something out there”, he says. “Focus on the learning of mistakes. Not the mistakes”. In addition to the learning opportunities that can be obtained from small failures, taking smart risks and persevering through challenges may help young professionals get noticed by people who can help them grow their careers. “There are a lot of people out there who are successful who really appreciate that quality: Smart risk taking, because you focus on the experience”.
On finding mentors:
Professional mentorship is valuable in most fields, and engineering is no different. Especially when starting significant projects, it’s important to have someone – or ideally a few people – with more experience than you to bounce ideas off of. “I have some mentors and people that are helping us build our company and I’ve been very fortunate to have them… be a resource”, says Justin. “They ask good questions, ‘well how do you feel about it? And what’s the downside? And what’s the upside? What don’t you know? Then go for it. That support group is really helpful. I would recommend getting some people 10 or 15 years senior, and a lot of people want to help.”