FiT Feature: Eric Daza

Name: Eric J. Daza

Title: Lead Biostatistician (Data Science)

Age: 43

Company: Evidation
How long have you been in tech?

I’ve been in health tech for more than four years. This is my second career, having previously spent seven years in clinical trials biostatistics.

How did you know you wanted to get into tech?

I got into health tech through a few statistically significant events.

I’d earned a neurobiology degree in college, followed by a masters degree in applied statistics. I then worked as a biostatistician in the pharmaceutical industry (cancer clinical trials) for five years. During that time, I grew obsessed with trying to understand the underlying statistical concepts I applied at work.

Statistics was really hard. I’ve written about how it’s one of the most counterintuitive fields I’d ever encountered—and how it kept me wanting more! So I went back to school.

I spent eight years in a public health doctoral program for biostatistics, followed by three in a postdoc for health promotion and disease prevention. During that time, I realized that if I did return to industry, I no longer wanted to be a clinical trials biostatistician.

That role generally involves designing and running analyses for testing ideas about well-defined research questions. It also requires a lot of procedural and regulatory knowledge—and the ambition to build these skills.

I’d developed a healthy appetite for making sure my teammates and I followed proper statistical procedures. But I didn’t want to do this full-time. I wanted to explore, to generate new hypotheses and discover insights using new streams of personalized data. I wanted to apply what I knew to new types of data to really improve and impact each person’s health day-to-day.

That’s how I got into health tech.

What made you interested in Data Science and what keeps you passionate about it?

Data science in my experience involves a lot of ideation and creativity. In contrast to most clinical trial biostatisticians, my fellow health data scientists and I design and run analyses for generating new or better insights (hypotheses) from vaguely defined research questions. We also use novel data sources new to pharma. These are the kinds of questions and data you find at the edge of knowledge—in my field in particular, at the scientific frontier of health behavior, psychology, clinical science, and medicine.

The role requires statistics, sure. And most folks know that data science asks you to be something of a technical generalist. You have to be open to learning and using what you decide to be the most appropriate analysis methods in a timely manner. Learning how to avoid “analysis paralysis” takes time, but it helps you efficiently own your decisions in order to deliver quality results on time.

But data science also asks you to build your communication skills at all levels; to align with your team, your clients, and the wider orgs involved. And in mid-stage health startups in particular (in my experience), you need to be willing to identify, propose, and lead the development of workflows and processes—all while doing the actual analysis work! These standard operating procedures help your org scale and automate.

As a statistician, I never thought I’d get excited about developing and enforcing “good statistical practices”—but here I am! It’s a great fit for me: I’m one of the few doctoral-trained statisticians in my org, so it’s one of the unique ways I can contribute to our data science teams.

Creating rigorous statistical procedures that my teammates will actually use is a workflow area that many see as a necessary data science chore. But this area actually excites me. Following and documenting your analytical process is crucial to doing good science and scientifically interpreting results correctly!

And I haven’t even gotten to my core research area and passion project: digital health “n-of-1” study design and analysis. You can read all about that in my 2022 interview in Forbes, and at my blog (and podcast), Stats-of-1.

Do you feel represented in tech?

Somewhat.

During and after my postdoc, I was pleasantly surprised to learn about the sheer number of Filipinx’s there are in tech. It was super interesting to hear about how the South of Market (SoMa) neighborhood in San Francisco, home to most of the city’s tech companies (and the global publicity that brought), was designated as a Filipino Cultural Heritage District by the city in 2016 because of its historical, long-standing Filipino population.

Still, I feel we have a ways to go. It’s important to make sure the tech world knows not only that we Filipinos are everywhere in tech, but that we’re also leaders!

In this spirit, I’ve tried to represent our community in my own work. At talks, I finish my slides with “salamat”. I introduced the “pancit plot” for graphing your own data. And I even coined the word “esametry” to describe my entire field of study based on “isa” (Tagalog for “one”).

Do you feel supported by the Filipinos in tech community? (in general not this org)

Yes!

My initial foray into data science began during my postdoc. While there, I tried to find other Filipinos in data science.

That search led me to Filipinx Americans in Science Technology Engineering Arts and Mathematics (FASTER)—and of course, Filipinx in Tech. I attended a few conferences and meetings of both groups, and was heartened to find others like me in the field. I even gave a few short FASTER talks, and helped lead a professional networking roundtable.

I’ve very much enjoyed getting to meet, network, even mentor (and learn from) other Filipinos in tech through these orgs and others. Related orgs include Pinoy Scientists, the Association of Filipino Scientists in America (AFSA), and the Pilipinx-American Public Health Conference (PAPHC).

What advice do you have for other Filipinos in tech that are having issues growing and obtaining promotions at their company?

It helps to have a manager who genuinely supports you, who really tries to understand the parts of your culture relevant to your professional context and career development.

If you have such a manager, make sure you “manage up” by communicating how you define success, how to track or measure your performance, and how to communicate your performance to them and to senior leaders responsible for your professional growth and promotions. Don’t worry about being perfect! A good manager will help you define these things, and will also acknowledge and learn from their own mistakes in helping truly set you up for success.

Find and make allies among the senior leadership of your organization. Make them aware of your ambitions. That way, when it comes time to decide on promotions and career development activities (like who to send to trainings, workshops, or conferences), they will generally know what you’re trying to accomplish and how to assess your progress—with your manager’s input, of course.

What advice do you have for other Filipinos that are interested in getting into Data Science?

“Find a way to your passions — may they serve you well.” Read my 4-minute blog post on how building a (healthy) obsession for quantitative analysis helps me thrive in data science.

Also, network network network! Ask working data scientists what it’s like for them. What does their day-to-day look like? How do they feel about their work? What would they like to learn or do? How could data science be improved? Do you feel like you belong at your org? Do you feel like you are heard and included in decision-making?

To get a good snapshot of daily experience, ask your friendly neighborhood data scientist to answer at least one of the following: What important skill did you learn last week? What important skill were you working on last week? What important skill would you like to develop or work on? (I totally stole these from my manager’s weekly update questions—shhhh!)

What skillset/experience do you think is most beneficial to be able to pivot into data science?

In a 2021 interview with Towards Data Science, I was asked the question, “Looking into the future, what are your hopes for the field of data science in the next couple of years?” My reply applies to this question, too:

“For now, I hope ‘data science’ writ large becomes formally recognized or defined as something like ‘a business or business-paced enterprise made up of teams of scientific experts and specialists.’ Core competencies should include strong fundamentals in management/consulting practice, statistics, and computer science. This will help schools and training programs create curricula that better prepare data scientists for the truly fascinating, inspiring, and fast-paced work they will do.”

Where do you see yourself in 5 years?

From my 2022 Forbes interview:

“I helped bring digitally enabled, individual-focused ‘n-of-1’ real-time health and fitness monitoring, diagnosis, and treatment to standard health promotion and clinical practice—for everyone, not just those privileged like me. Think of behavior-change recommendations, treatment plans, etc., that are truly scientifically rigorous in assessing causation, not just correlation, for each person based on their own unique recorded health history. On a grander scale, I helped shift the focus of healthcare research from our traditional population-based ‘nomothetic’ statistical study designs and methods to individual-based ‘idiographic’ ones (where sensible and feasible).

…

We could meaningfully improve the quality of daily life for folks suffering from distinctive recurring conditions.

How? Through quantitative idiographic (QI) approaches like n-of-1 trials and single-case designs, and their cousins, the individually adaptive approaches of JITAIs, MRTs, and SMARTs.

…

QI approaches could also improve ‘precision medicine’ by helping discover groups of patients that respond in particular ways. Alongside their genetics, each patient’s own measurable health habits and behaviors can be used to define their own baselines for tailoring diagnoses and treatments; the ‘nature and nurture of you’. QI studies could also better disaggregate group-based health disparities from the bottom up: Start with each individual’s own quantifiable experiences, then aggregate up to find general patterns useful for making more nuanced community-wide recommendations, practices, and policies.”