How Raphael Chen Transitioned His Career From Chemical Engineering to Data Science and Found Job Success in Just 4 Months
Raphael is a bright and ambitious graduate who previously studied an integrated Master’s degree within Chemical Engineering & Management. Through his working experiences, he decided he wanted a career change. Data science appealed to him greatly.
By taking a leap of faith, he enrolled in our data science & AI part-time course. Through hard work and dedication across the 24 weeks, he successfully adopted the most in-demand and relevant skills in today’s digital world – and found himself with a job offer before he’d even completed the program.
Raphael now practically applies numerous skills learned from our certification program within his data science role with CARRO in his day-to-day work. His future career paths have been expanded massively.
We’re so proud of his progress and cannot wait to continue following his future successes in the industry.
Read his inspiring story here:
Can you describe your career experience before making the change to data science & AI?
“Before starting the Institute of Data program, I graduated with an Integrated Master’s degree in Chemical Engineering with Management. I was a 2021 fresh graduate and started working at a Venture Studio as an analyst. I enjoyed the experience in the Venture Studio but wanted a career that would be more technical-focused. Whilst I was studying for my Master’s degree, I was exposed to data analysis through my Red Blood Cells research topic and that was how I became interested in data science as a potential career path.”
What were your career goals before upskilling to data? How has your outlook changed?
“I wanted to pursue a career where I can effectively apply my skills & knowledge across different business contexts in a unique way. When I came across data science, I saw it as a powerful tool rather than just a job. My outlook has definitely changed as I realised it was It’s about how you utilise different data science tools to contribute to different business problems in a company, and not necessarily just working on complex modelling.”
Why did you choose to join the part-time program in particular? What appealed to you?
“I chose the part-time course as I was also working full-time, so the 6-month period of time worked well for me. Furthermore, I liked the way how the course modules were spread out over the 24 weeks as I needed more time to digest the materials after each lesson. All in all, it was a suitable amount of challenging materials within a good time frame. ”
What was the most challenging aspect of completing the course?
“The most challenging aspect of the course was staying consistent in my learning & efforts. Without the support in the cohort from my trainers and peers, I would have found the course quite challenging. My cohort was very small, which allowed me to share my problems and be supported greatly whenever I come across any problems. ”
Tell us about your capstone project! How did you come up with your topic?
“My capstone project was focused on figuring out how to help fintech companies better assess the loan applicants’ repayment abilities when giving out financial credit loans. Covid has caused a rise in individuals taking out more personal loans. It is important for financial firms to understand how to categorise which type of borrowers will most likely default on their existing loans as this will affect these firms’ credit risk ratios and liquidity. There are many companies that are focused on this area – such as Afterpay and Revolut. They are looking for new ways to assess an individual’s credit worthiness and tap into this evolving market. Therefore, it was a topic that I was really interested in.”
How did you find the process of completing your final Capstone project during the course? What did it teach you?
“I did struggle quite a bit due to the lack of financial background, but overall enjoyed the process of completing the Capstone project. Credit is all about offering money upfront based on the trust that payment will be made in the future, and you never know when it will benefit you. I have gained a lot of insight into how the credit industry works and was able to practically implement some of the skills learned in the course – for example using Python libraries (NumPy, Pandas, Matplotlib, Seaborn, and Sci-kit learn) for data cleaning, and exploratory data analysis to obtain key insights and understand what are the important features to take note off before implementing different machine learning models.”
What are some tips you would give someone preparing to present their Capstone project during the program?
“My biggest advice is to try to keep the presentation as simple as possible. Simplifying the language and making your message very concise can be one of the most important skill sets to develop in the data science industry. Practising beforehand is also very important. Have a walkthrough of the presentation, time yourself, and make sure you get to the point of your presentation. The main objective is to always relate the technical aspects to the business context so that your audience will understand the full picture of your project. ”
You’re now working as a Data Scientist with CARRO – Congratulations! How did you get a job in the industry before even graduating from the program?
“I actually got the role in a very non-traditional way! I had a short conversation with the HR manager at CARRO on Linkedin and it quickly transitioned into a 1-hour phone call. During the entire period, I didn’t actually have much time to prepare beforehand as it was an unexpected interview but I quickly realised the interview was more to assess my intentions behind moving into data science. After the interview, I was asked to complete an online technical assessment followed by a 36-hours take-home assignment. Once my technical assessment was reviewed, I had a final interview with the Chief Data Scientist before receiving an offer from CARRO.
CARRO is a very ambitious company that is fast-paced. I joined at the end of November 2021 last year. Their approach is very iterative – fail fast, pick yourself up, and learn on the job. I’m really grateful to have this opportunity and I’m enjoying my work with them. Overall, they are a very supportive and helpful team.”
How do you prepare for a data science interview? What are the most important things to remember and discuss with a potential employer?
“As mentioned before, I actually didn’t have time to prepare as the interview was quite unexpected. They were more focused on understanding why I was wanting to move my career into data science – especially as I was coming from a chemical engineering background. Overall, the intention was to make sure I do come in with the right expectations as it is a steep learning curve in the data science field. My advice is to have grit and just keep on trying in your job search! Persevere, learn from your mistakes, and you’ll definitely attain your career goals.”
What kind of professionals would benefit from an accelerated training program like this?
“I believe anyone with the right mindset and hard work can make this career change. With that said, I do believe applicants with a STEM background will find it easier to transition their careers into data science. They usually have a strong mathematics background and may be familiar with some programming already. From there, the course can fill other gaps such as fundamentals of data science, machine learning models, and storytelling skills.
I think a lot of people have misconceptions about data science as it is actually a very multidisciplinary field that requires a lot of understanding about the implementation of data science in businesses. There are many aspects that come into play – such as problem-solving skills, model deployments and most importantly having strong business communication skills. It definitely is a hard career to move into but I believe anyone can do it with the right attitude & mindset.”
In your opinion, how did the program change your perspective on what is required to be a modern data professional?
“After taking the program, it has made me realise that data science is a lifelong learning journey. It is impossible to know and understand everything straight away. I didn’t know data science was going to be so interdisciplinary, and how it integrates into different business contexts. All in all, it is very important not to get too bogged down in the technical details and always look at the bigger picture. ”
What is one thing you know now that you wish you knew before changing careers to data?
“I wish I knew the extent to which data science would cover. I didn’t realise how intricate it would be, and how steep the learning curve would be in the course. The course was very practical and hands-on which definitely helped. Maybe if I had gotten myself more exposed to a few simple but different types of projects beforehand, it might have prepared me better. ”
Connect with Raphael on LinkedIn here.
If you are interested in up-skilling and landing a new role in the data science field in Singapore, schedule a consultation call with a Data Industry Career Consultant today.