Posted by Pranjali Galgali
Updated: Aug 30, 2019
For nearly three years, Bernard Ong has held the position of AVP, Lead Data Scientist, Advanced Analytics at Lincoln Financial Group. Before that, he enrolled at NYC Data Science Academy to immerse himself in data science. Since 2016, he has stayed connected with the school, which he finds to be a key source of hiring qualified candidates in his data science team. Recently, he met with our instructor Drace Zhan to share some insight into his career path along with some tips for future data scientists.
I have a master's degree in MIS (Management Information Systems) on top of a BS in Mechanical Engineering. I already had a strong technical foundation in the field from having worked in the industry for a very long time on two major cities: in New York and Tokyo. After working in the industry for so many years, I started to wonder- What else is there?
I got the answer from my son who was studying data science in college at the time. He said it seemed to be the next "big thing". That's when I started exploring the field, reading and conferring with people to discover the opportunities and challenges for data science jobs and education.
I considered going back to school to get another master's degree, but five years ago (when I was going through this process), there weren't data science programs at many universities. To make a long story short, I looked at the available options and investigated what each bootcamp offered, and NYC Data Science Academy stood out to me. Specifically, I appreciated the rigor with which NYC Data Science Academy evaluates candidates because the reputation of a program is really important to me.
Another benefit of a bootcamp versus another master's degree is that a bootcamp is more than just completing coursework and moving on. It provides a supportive network for job guidance and support. I continue to benefit from it even now that I am on the recruiting end.
The title of "Data Scientist" implies a wide variety of skills. It entails not just technical skills, statistics, and mathematics, but also domain knowledge and expertise. It's about using tech to come up with solutions that meet business needs and having the ability to communicate through data visualizations and storytelling.
A lead data scientist has to possess all those abilities and be on top of the technology to effectively manage a team.
This industry is changing very rapidly, and as a lead, I have to know what is cutting-edge, what is practical, and what needs to be researched more before it is ready for prime time. As models and algorithms come through the pipeline at a rapid pace, a leader needs to have hands-on experience to effectively steer the team.
It is equally important to understand the business end because you need to be able to serve as a bridge between the data scientists and the business teams and to translate the process in terms understandable to each. The lead data scientist also has to have good intuition when it comes to selecting new team members and needs to foster a spirit of collaboration.
The main challenge is finding the right people for the team. It is important to realize that there are no perfect candidates. Even while the supply of qualified people is growing, the demand remains very strong, and so the truly good ones are hard to find. You have to know which particular skills are needed for your team and find qualified people that specialize in different aspects and who are a good fit for the company culture. To accomplish that, you need to do a very focused search. This is where I have an advantage as part of the NYC Data Science Academy alumni network.
It's almost like getting to recruit for free. The school's network is an invaluable resource not just for graduates seeking opportunities, but also for companies like mine. Given my personal experience with NYC Data Science Academy, I know what I can expect from the program graduates. This allows me to work with the academy to get qualified candidates.
The advantage for me is that I can hire faster, which is a huge benefit for the company. In some companies, positions remain unfilled for three to six months, which impedes progress. Also, finding candidates through the academy spares me the very painful process of working with recruiters who don't understand the industry and exactly what I'm looking for.
Of course, a good grasp of data science is critical, as are skills in math, statistics, and technical aspects. But what I'm looking for is someone who understands how to apply those skills, to make that bridge from theory to practice. They need to understand what's behind the algorithms. They have to understand the "why" behind the black box to know when and how to use the models. I look for the ability to bridge those two worlds together so that the new hire will be able to understand the business side of reducing cost, improving revenue, etc.
I refer to the qualities I look for as the three C's: curiosity, creativity, and critical thinking. When you want to compete effectively, you have to be able to not just understand modeling and predictive analysis, but also what kind of business challenges we are trying to address. It begins by asking the right questions, which stems from curiosity. It continues with critical thinking to assess the problem and progresses with creativity to come up with innovative solutions. Then you have to communicate the vision to the business end in terms they understand.
My suggestion is to talk to people in the industry to hear their experiences and how they got into data science. That will give you a high-level perspective on the process, and its pros and cons.
It's a very exciting field with a lot of opportunities. So, it's up to you to choose the avenue that makes the most sense to you. Try to find mentors and sponsors to help guide you along the way.
The diverse mix of students at NYC Data Science Academy can offer you a lot of perspectives. Working with your classmates teaches you how to work with people from many different backgrounds, educational levels, and career levels.
The projects are useful in teaching the application part of data science. They use Kaggle and industry data and allow you to apply the skills you've learned.
The general advice for those who are interested in the field: go out and build something that offers benefits. Find relevant challenges that answer business imperatives. You can effect change in the world or community. Our work does not end with developing a model. When you find a model that works, you have to adapt it to scale so that it becomes operational for customers.
What a bootcamp offers is an immersion into the entire field, the broad spectrum we talked about, and the technical aspects of data science. The hands-on experience is invaluable. Think of it as a fast track. It's the opportunity to shift your mindset. It is effort well spent that will pay you back in spades, not just in terms of the skills you learn, but in terms of the support and guidance, you gain from the people you meet. They are there to help and advise you and steer you on the career path that is right for you.
Pranjali Galgali is a Marketing and Communications Associate, NYC Data Science Academy. She is a Master's in Digital Media and Strategic Communications from Rutgers University. She enjoys reading and writing about data science, upcoming technologies and loves interviewing people.View all articles
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