Rachel Mak-McCully
Rachel Mak-McCully is a data scientist with a passion for building data-driven tools that empower people to improve their health. She received her PhD in Neurosciences from UC San Diego studying sleep and also completed two postdocs in EEG research labs. As a data scientist working in industry, Rachel specializes in extracting meaningful signals from noisy health data and communicating these results to a range of stakeholders. In addition to her research contributions, she is the recipient of multiple awards including the NSF Graduate Research Fellowship, a Chateaubriand Fellowship, and a Fulbright Scholarship to France.
Can you describe your academic and professional background? What path led you to pursue this field?
My PhD is in Neurosciences, with a focus on the neurophysiology of sleep. Sleep intersects with so many aspects of health and well-being, but is still quite a mystery. I found the topic challenging, intriguing, and applicable to everyone’s health.
My PhD work really focused on the mechanisms of sleep generation in the brain; as a postdoc, I wanted to better understand how sleep impacts health. When it came time to decide whether to continue in academia or to pursue a career in industry, I reflected on what I really enjoyed about my work. For me, I wanted to analyze data every day, which a professor usually doesn’t have time to engage in directly. Wanting to solve complex problems related to people’s health and to work with data every day, I landed on pursuing a career path as a data scientist. In order to break into the industry, I did the Insight Health Data Science bootcamp. I’ve been fortunate that my two roles as a data scientist have been in neuroscience-based companies.
Can you tell us about your current responsibilities? What is a typical day or week like in your role?
Currently, I am the lead data scientist for sensors & wearables at Octave Biosciences. I am responsible for integrating data coming from sensor & wearable data into our comprehensive care platform for people living with MS. The advance of sensor & wearable devices means changes related to MS, but also general health and wellbeing, can be monitored on a regular basis between neurologist visits. What’s most interesting about this role is that there is no blueprint or roadmap for what we are building. Most devices in the market are directed directly to consumers, for use in clinical trials, or for research purposes. We sit somewhere in the middle of all three. This means that I don’t necessarily have a typical day or week. I talk a great deal with potential companies to understand the data collected, how it’s been validated, and how we can access and integrate that data into our work flows. I also get to test a lot of solutions myself & coordinate others in the company testing the solutions as well. I spend a lot of time evaluating the data coming from the devices & understanding how the data are meaningful in clinical practice.
What do you enjoy about your current job and work environment?
What I enjoy most about Octave and my role there, is that while I get to work with lots of interesting data, I also get to understand the product and business sides of what I’m working on. In larger size companies, this 360 view wouldn’t be possible. Octave does really foster a culture that if you think something is important or there is something you are interested in pursuing, you have the support to go forward & spearhead that effort.
What are some of the challenging aspects of your job? Is there anything you wish you had known about your job or industry before joining?
It is true that in a start-up, you wear many hats. Priorities often change quickly. I am much more comfortable having one project or set of data that I dedicate most of my time to, and then move on to the next project or data set. As we are building out a solution that hasn’t existed before, it has been a growth experience for me to balance a lot of competing demands and be flexible in needing to pivot quickly if directions change.
Do you have any professional plans for the future? What are some future career paths that could open up for someone in your position, 5-10 years down the road?
What I have learned over the past 5 or so years, is that you can have a professional plan for the future, but there are a lot of things that happen both personally & professionally that can change that trajectory. And that’s okay.
What’s changing in your industry? Are there any future trends we should be aware of?
Data science used to be a catch all term, and what a data scientist did could really vary from one company or role to the next. I think we are seeing an emergence of more specialization; for example, a data analyst might spend most of the day writing queries of the data and visualizing the data, while a data engineer builds out data pipelines, a data scientist analyzes data and builds models, and a machine learning engineer optimizes models. In the past, I think a data scientist was expected to have all these skills—and expected to pass an interview that tested all these skills--although in reality their day-to-day job was a much narrower focus.
Is it common for people in your field to have a scientific/academic background (i.e. have PhDs)? Can you think of any advantages or disadvantages someone with a PhD might experience while pursuing or working in your field?
Traditionally, data scientists had a scientific/academic background, and especially a PhD. With the explosion of data science, that is no longer the case. I have worked with some really good data scientists who do not have a masters’ or PhD. However, what they do have—and what a PhD gives you rigorous training in—is understanding your data. How it was collected, what you are seeing, potential problems in the data, skepticism about your data. Classes on data science often focus on the machine learning aspects of data science. That’s the last 10-15% of your work as a data scientist. And for many problems you will encounter in industry, a logistic regression or random forest will often be as ‘advanced’ as you need. In industry, you really need to understand the use case of what you are building.
A disadvantage that I’ve seen is needing to learn to move on—either because we’ve learned what we need to or because what we’ve found doesn’t match business needs. As scientists, we are used to failed experiments; however, normally you would try to understand why it failed, or make a tweak in our protocol and try a new set of experiments. For example, I might spend a lot of time on a particular device that we think could be integrated into our platform, but it’s not quite what we need right now. Rather than pushing on trying to make it work as we need it to, I need to move on to another device that might better fit our needs, while keeping that device on the back burner as it evolves.
Do you have any final words of advice for those navigating these career questions? Is there anything you would have done differently given what you know now?
I can’t recommend informational interviews enough! There are so many different career options now for people who choose not to stay in academia, but there are not always clear-cut paths on how to pursue those options. What I have particularly learned from start-ups is that oftentimes, if you are dedicated, curious, and you want to learn, that counts much more than having a niche set of skills. Also, a lot of jobs are never even publicly posted, so it’s good to meet people and let them know you are looking.
I would suggest starting with people you know, or friends of friends who have jobs that might be of interest. Ask about their path, what their job entails, what they like/don’t like, what they wish they would have known before they started. People are busy so I recommend asking for a specific amount of time—like a 20 to 30 minute chat—and just very generally what you would like to cover. Come prepared with questions. I also can’t recommend showing gratitude and appreciation enough. Send a quick thank you email for their time and when you do land your position, reach out to let them know where you ended up and to acknowledge their bit of help on your journey!
