Admond Lee is a data scientist, speaker, and data science contributing author at several publications including Towards Data Science, KDnuggets, and AI Time Journal. He has recently been nominated as one of the AI Time Journal inspiring data scientists to follow in 2020.
We thank Admond for sharing such accurate insights in this interview, including the importance of soft skills in his profession such as storytelling, his attitude towards acquiring business-domain knowledge, and valuable resources that helped him along the way.
This interview is part of the Data Science Interview Series 2020.
How did you first get into data science?
It all began when I first joined the Summer Student Programme at CERN in Switzerland. CERN is a European research organization that operates the largest particle physics laboratory in the world.
The CERN Summer Student Programme offers once-in-a-lifetime opportunity for undergraduate students of physics, computing and engineering to join one of their research projects with top scientists in multicultural teams at CERN in Geneva, Switzerland.
Check out what books helped 20+ successful data scientists grow in their career.
In June 2017, I was very fortunate to be accepted to join the programme. I literally burst with joy as particle physics have always been my research interest and being able to conduct the research at CERN was simply a dream-come-true-experience for me! During the 2 months internship period, I did some analysis and simulation on the event reconstruction of terabytes of data via Worldwide LHC Computing Grid & Cloud Computing for Compact Muon Solenoid (CMS) Experiment.
Besides, summer students also attended a series of lectures, workshops and visits to CERN facilities that covered a wide range of topics in the fields of theoretical and experimental particle physics and computing.
During this period, I was introduced to Machine Learning and big data analytics by the lectures, workshops, and even my project itself. I was particularly mind blown by how these Machine Learning techniques could be used to classify and detect various microscopic particles to an extraordinary precision with such a huge amount of data. Baffled, I took a deep dive into Machine Learning and cloud computing topics without hesitation, simply because I loved it!
I was amazed by how data could be used to generate insights and drive business values for companies. From understanding a business problem, to collecting and visualizing data, until the stage of prototyping, fine-tuning and deploying models to real world applications, I found the fulfilment of tackling challenges to solve complex problems using data. Gradually, my passion began to take form…
Interestingly, my experience at CERN was my starting point and journey from physics into data science.
How is data science used to create value in your current projects?
My projects revolve around bringing smart manufacturing and AI solution to the current company. Therefore, tons of data are analyzed using statistical, machine learning, or deep learning approach.
What are the key skills that you use every day as a data scientist, and how did you develop them?
In my opinion, there are two main key skills that I use as a data scientist —technical skills and soft skills.
Technical skills include math and statistics, programming skills, and business knowledge. I was fortunate that I started laying my foundation in math and statistics when I was a Physics student back in my university days where calculus was a hardcore subject to learn. However, if you’re just starting out, I’d recommend you to check out some crash courses on Udemy to learn and practise math and statistics.
Similarly, I started learning and honing programming skills through Udemy courses, internship projects and personal projects. The journey was not easy, but it was one of the most rewarding journey that I’d ever had.
In terms of soft skills, I’d emphasize on storytelling skills and teamwork. As a data scientist, I typically work with different team members and stakeholders from various teams and departments. Therefore, having the ability to convey messages effectively and work together as a team is crucial to make projects successful.
What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
One of the top challenges that I currently face is understanding business problems and scoping them into clear problem statements.
I tackle these challenges through detailed understanding of the business nature, process and knowledge by working with many stakeholders. Subsequently, I’ll work with stakeholders to make sure our expectations are all aligned and that the problem statements are what we aim to solve with well-defined metrics.
How important is the domain knowledge of the business/industry you’re in as a data scientist, and how did you acquire it?
I’d say the domain knowledge of the business/industry that you’re in as a data scientist is extremely important. Having the domain knowledge gives you an edge when it comes to understanding the meaning of data, interpreting models’ results that make sense in the context of the business, and conveying insightful results and actionable insights to stakeholders.
Personally, I acquire domain knowledge by constantly asking questions to make sure I grasp the technical jargon in the business and how the business operates as a whole.
Do you create data science content?
With my passion to help aspiring data scientists land data scientist jobs, I also created an online course (Data Scientist Personal Brand Toolkit) to allow them to stand out and ultimately land data scientist jobs through building a strong personal brand. Check out this page for more.
3 words that best summarize how you learned ML and data science:
passionate, consistent, self-motivated
People: who are some inspiring data scientists and people in AI that you follow?
Ben Taylor, Cassie Kozyrkov, Andriy Burkov
Books: which books have helped you the most in your journey and why?
This was the first book that I read when I first started my journey into data science. It helped me understand the fundamentals of machine learning with intuitive and clear explanation. This gave me a strong foundation and confidence to get started to tackle data science problems, which is very important for a beginner in data science.
Courses: what courses/programs have you taken that have significantly contributed to advancing your career in data science?
This Udemy course (Python for Data Science and Machine Learning Bootcamp) was the first online course that I took to get myself started in data science. I began honing my programming skills in Python and starting using some of the common machine learning libraries to solve data science problems.
This paved a crucial foundation to me as I continued my journey in data science, which subsequently advanced my career in data science.
What are the top 3 resources that you use to keep up with the advancements in the field?
LinkedIn, Medium articles, News
What is the biggest improvement that you introduced in the last 12 months that has considerably improved your workflow?
Storytelling skills. This is one of the most important skills that we can learn as a data scientist in order to simplify complex problems and convey core messages more effectively to stakeholders from different levels. This has tremendously improved my workflow as a whole.
What advice would you give to someone who wants to get into data science today?
Take massive actions towards achieving your goals as an aspiring data scientist. No action is too small to make a difference. Just move forward one step at a time. When you’re on the verge of giving up, PERSISTENCE is key.
Your favorite thing about working in data science:
Being able to make an meaningful impact in the business and help others through data science.
If you weren’t working in data science, you would be:
A particle physicist
What inspires you about working in Data Science?
The boundless opportunities to make an impact in the world.
In particular, I am amazed by how data could be used to generate insights and drive business values for companies. From understanding a business problem, to collecting and visualizing data, until the stage of prototyping, fine-tuning and deploying models to real world applications, I love the fulfillment of tackling challenges to solve complex problems using data.
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