Current Position and Field

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Q: Can you describe your current position and what field you work in?

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A: Sure, I’m a data insights engineer at Flat Iron Health, a health technology company that provides an EHR system to oncology practices. In this role, I've been on various teams that look at our EHR data, link it with external data sources like biomarker testing results, perform QA on our data before we sell it to clients, and use it to identify whether a patient might be a good candidate for a clinical trial.

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Typical Day at Work at Flatiron Health

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Q: What does your normal day-to-day look like?

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A: A normal day for me is probably 30% meetings, 30% writing code, and 30% communicating with people asynchronously.

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Anthony's Career Journey Overview 

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Q: Can you give a quick overview of your career journey, like what college you went to, how you got interested in the work you’re doing now, and any previous jobs related to what you're doing now?

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A:  Sure, my career path started in hard science. I have a degree in physics and became interested in using computers to solve problems while working on research as part of my physics degree. I realized that it was something I found exciting—using computers to solve scientific problems. Then, I worked at your dad's company as a data analyst for educational data and other claims data. Eventually, I made my way to Flat Iron Health, where I’ve been able to use and further develop my computing skills. It’s been pretty rewarding for me.

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What is Cutting Edge about Anthony's Career

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Q: What would you say is most unique or cutting edge about your career in terms of its difficulty and the rapidly evolving market it is in?

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A: I think data science is interesting to people as an emerging career because it combines a set of skills in a new and interesting way. To be an effective data scientist, you need to be good at communicating with people from both technical and non-technical backgrounds, have some quantitative skills like statistics, know how to use computers to solve problems, and be familiar with data challenges like having too much or too little of it. These skills haven’t historically been required of a single profession until recently, when this niche of data scientists emerged. However, the role can vary depending on the company, so it’s not always well-defined. But generally, it allows you to use different parts of your brain in the same job.​

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Emerging Job Opportunities in Data Science

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Q: In your current field, what do you think are the most interesting career opportunities emerging for people entering the job market in 5 to 10 years?

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A: We've seen a huge explosion of interest in AI, large language models, machine learning, and related fields. A lot of people are responding to that excitement, not just in the industry but also in mainstream media, by getting into this kind of profession. There will definitely be a part of the economy for people with these interests. But it’s also worth emphasizing that there are many other rewarding careers out there that don’t heavily involve AI or ML, which still require solving hard problems in unique ways. There will always be companies with data problems to solve.​

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Advice for Aspiring Data Scientists

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Q: Do you have any specific advice for a young person interested in your field? Any work they should get involved in before becoming more advanced?

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A: For a younger person interested in this field, I'd say get some computer programming skills early—start playing with computers. It’s great if you have a reason to do so, especially outside of your coursework. Pick up a toy problem, pretend you’re starting a company, or actually try to start a company—just find an excuse to do this kind of work. Best case scenario, you’ll have something cool to put in a portfolio, or it might even turn into your job. Also, having a rigorous background in something like pure math or hard science is always respected by interviewers and companies. So while data science programs are great, don’t be afraid to take on something adjacent that interests you and then pivot into something more applied.​

 

Career Reflection

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Q: Looking back at your journey so far, is there anything you would have done differently?

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A: When I first started my current role, I thought I might be most valuable as someone who could sit in a corner, solve hard problems, and then send them back to the team. I’ve been able to do that somewhat effectively, but I’ve realized that the most efficient way to add value to an organization is by leaning into collaboration, over-communicating, and solving problems together with stakeholders. So if I were to do things differently, I’d focus more on collaboration rather than solving problems in isolation.

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Future Goals for Anthony

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Q: Do you have any short or long-term plans or goals?

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A: Right now, I’m on a team trying to scalably identify patients who are good candidates for clinical trials. My short-term goals involve doing that well, helping to stand up the team, and being able to take on new work more calmly. Long-term, I’m considering transitioning to managing people at some point. I’ve heard stories from people who found value in alternating every few years between being a manager and an individual contributor. One way to grow your impact is by using other people as resources to solve problems, which increases the scale of what you can achieve. So, it’s something I’m considering for the future.​

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Anthony Description of Data Science

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Q: What’s your personal idea of what data science is?

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A: I think data science is the application of the scientific method to data that someone else collected, often for commercial reasons or by accident. It’s a broad field, and there are many different roles and titles related to data science. For example, my official title is Data Insights Engineer, which might be called data science at another company. It involves using skills related to math and statistics, computer programming, infrastructure tools like ETL tools or Tableau, and, importantly, communicating well with stakeholders of varied backgrounds.

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Specific Applications of Data Science

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Q: I hear the term data visualization linked with data science but how does this term actually apply?

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A: Spreadsheets are one example of how people visualize data, but there are other examples too. People sometimes build dashboards that allow you to draw plots, tables, and figures that refresh automatically and consume data from a database. For instance, if you have an e-commerce company with a database that records every order, what was bought, how much was paid, and when the transaction occurred, your boss might ask you to analyze spending patterns on weekends versus weekdays to inform marketing decisions. You’d then build a dashboard to help understand that, refresh it live, and even use it to measure the impact of marketing interventions. That kind of work would fall under the scope of data science.