FiT Feature: Karl Elamparo, CFA
Title: Data Scientist
Age or Age Range: 33
Company: Meta
How long have you been in tech? 5 years
How would you compare the tech space in Manila versus the Bay Area?
I can’t do the Manila tech scene justice as I’ve only worked in Finance there. Having said that- I’d wager that network effects are more pronounced in the Bay. One is exposed to large talent pools and opportunities for organic ideation on a day-to-day here.
How did you know you wanted to get into tech?
I’d say it was when I read the 2012 article "How Companies Learn Your Secrets" from the New York Times. I was a fresh graduate trying to break into investment management, and here was this burgeoning use of so-called Machine Learning techniques to unlock value. It was fascinating and scary- and I dreamed of working with them (even though I never in a million years imagined that I would decide to).
What are the main differences from being a data analyst at non tech company a data scientist at tech company (if any)?
To caveat- this distinction is hotly debated, but from my personal experience, Data Science (DS) roles, as opposed to Analyst (DA) roles, typically entail heavier use of techniques which are business applications of Statistics, Mathematics, and Computer Science theory:
Machine Learning applications: DSes are expected to know when and how to use a variety of ML techniques for a given business problem (ML is a set of techniques which allow for predictions of outcomes based on underlying data). I don’t mean to imply that DAs don’t use ML- but generally speaking, DSes would know the ML development lifecycle from ideation to deployment.
Experimentation, Measurement, and Causal Inference: DSes should be able to understand how to design and conduct online experiments, and the techniques and tools necessary to inform decisions when such Experiments are not feasible: Causal Inference methods. To caveat~ in many companies, DAs actually run the experiments themselves, with DS as consultants on setting those up.
Programming skills: DSes tend to have a higher proficiency with Python and R. It would not be uncommon for DSes to program the tools they need, instead of relying on Software Engineers- tools such as Optimization, Simulation, Forecasting, etc.
What skills do you think are important to be successful as a data scientist?
Being able to drive the correct decisions to key stakeholders given ambiguity and uncertainty. Also- being able to influence the business towards the ‘right’ outcomes (implies one knows and can defend what ‘right’ means).
Being hungry for technical knowledge and having a growth mindset- in order to keep up with ever-changing technologies, modeling techniques, and methodologies.
Integrating statistics, Experimentation key principles, and the scientific method into every facet of decision-making. This also involves skepticism even unto one’s beliefs and assumptions
Were you supported by your family to get into tech?
Not really- they didn’t know anything about the Tech scene or Data Science. Also they’re half a world away :)
Do you feel represented in tech?
Filipinos constitute an extreme minority in Tech, and thus I do not feel we have strong representation.
Do you feel supported by the Filipinos in tech community? (in general not this org).
Not really.
What advice do you have for other Filipinos in tech that want to join a FAANG company?
Frankly- the most straightforward way is to get a Master’s degree in the career you want (Comp Sci for Software Engineers, MBAs for Product, etc) and take as many internships in established companies as you can. You’d want to pick a tech- or tech-adjacent company with strong Brand value to interviewers, and one that is still experiencing massive growth. Internships are also potential ways of leading to direct recruitment.
For those for whom getting a Master’s degree is not ideal, I would suggest focusing on learning and applying the Technical Skills that are required in FAANG job listings into one’s existing job. Youtube, Google, and now also ChatGPT, provide a wealth of self-learning opportunities.
For example, Filipinos wanting to get into FAANG DS should provide demonstrable expertise in personally conducting A/B tests, building ML models, and engineering tools. The key is to highlight that one’s individual efforts and accomplishments have benefitted the business greatly.
But in any case, companies value different things for the same position- for example, Meta DS requires strong leadership and decision-driving ability across all levels, even more so than technical or modeling proficiency, whereas OpenAI DS might require creativity and proficiency with LLMs more than leadership skills. Knowing each company’s culture/requirements and playing to those during the interview process are absolutely essential to maximize one’s chances of getting in.
What personal accomplishment are you most proud of (doesn’t have to be professional)?
Pivoting from Investment Finance to Data Science using Youtube and Google.
Where do you see yourself in 5 years?
Probably going deeper into more technical DS-adjacent fields such as Machine Learning Engineering or Research Science.
What would you like to see more of from Filipinx in Tech?
More of these sessions from various disciplines!