A journey to work as a data scientist in Canada
Get more insights from RBC data scientist
We recently caught up with Raman Preet Singh, an SFU Big Data Alumni from previous cohort and current a Data Scientist at RBC. We were keen to learn more about his background, his experience and his advice for students who are perusing a career in the big data area.
Hi Raman, firstly thank you for the interview. Let’s start with your background and how you became interested in working with data…
I did my under-graduation in Electrical & Electronics from India. After which I worked in a startup as an Oracle Apps SCM Consultant for a short period. Thereafter, I joined IBM India as an SAP ABAP Developer and worked there for almost 2.5 years. I had heard a lot about Big Data during my tenure with IBM, so I started attending meetups to get knowledge of this subject and soon developed an interest in the same.
To further pursue my interest, I decided to move to Canada as there were a lot of good schools which were offering Post Graduation in Big Data Analytics. So, I joined Georgian College’s PG certificate in Big Data in Barrie, ON which gave me a high overview of Big Data tools and technologies, mostly theoretical. To get more hands-on experience of the subject, I decided to pursue Professional Master’s in Big Data Program from SFU as it is a complete program with a perfect balance of Big Data and Machine Learning along with a mandatory coop semester. I was hired by RBC as an ML Developer coop for my coop term and was later offered a full-time position as a Data Scientist.
Q. The structure of the machine learning course has been changed in this cohort, which focuses more on machine learning theories and mathematical derivation. Do you think it’s better for big data students as we are inclined to work in practical aspects?
A. Personally, I feel it’s a good change as just applying ML models is not a big task and it’s something which can be learned quickly. But if a Data Science aspirant knows the mathematical background of how ML algorithms work, then I feel it can come handy in the real world as the real-life problems are more complex and you sometimes need to twist the algorithms or maybe create a new algorithm completely to make it work for your data/use-case. So, having a mathematical knowledge of the ML models can be of advantage in this case.
Q. With your multiple co-op and working experiences (software development, machine learning, data science) in the industries, do you have any suggestions or comments about our program’s course content or course structure?
A. As I said earlier, SFU’s curriculum is a perfect blend of Big Data and ML. One can get hands-on experience of working with Big data technologies like Spark, Hadoop etc. as well as ML algorithms. This opens up a lot of opportunities for the students to pursue the career of their choice. I know students getting into different roles like Data Engineer, Data Scientist, ML Developer, Big Data Developer etc. So, I feel the curriculum is structured pretty well which caters to individuals career choices.
Q. What’s the general satisfaction rate you would give for the SFU Master of Computer Science Program (Big Data Specialization)? (Choices very satisfied, satisfied, neutral, dissatisfied, very dissatisfied)
A. Very Satisfied.
Q. Among different areas you’ve worked in, which area are you most passionate about? How do you think about these different areas in 10 years?
A. I am more interested and passionate about Data Science as I get to solve some really cool and challenging real-world problems. Also, the role of data science can be fitted in any sector like banking, finance, e-commerce, telecom etc., therefore opens up a range of options for a data scientist to choose from.
I think every role has its own space, but data science has grown substantially in the last 5 years and the jobs in this area are estimated to grow significantly in the coming years. Since we are generating quintillion bytes of data every day, there is a huge potential to extract meaningful insights from this data and I feel data scientists could play a huge role to make value out of this data which in turn can resolve many complex real-life problems.
Q. What’s your main responsibilities as a machine learning developer and data scientist respectively? What do you think about the difference and connection between machine learning and data science in practice?
A. I did the same thing in both the roles, so can’t really comment on that. But generally speaking, Machine Learning developer is specifically focused on the algorithmic side of things like tuning, optimizing, parallelizing, testing and even deploying the algorithms, whereas Data Scientist role is more like a story-teller, taking the raw data, cleaning and pre-processing the data, analyzing, hypothesis testing, visualizing results etc.
Both roles require different skill-sets as ML developer should have strong programming, algorithms, data structures knowledge, whereas Data Scientist should have strong statistics, programming and business knowledge.
Q. What’s your team’s composition and what are the main tools and technologies your team use?
A. In my team, there are 5 Data Scientists and 2 Machine Learning Engineers. On daily basis, we use Apache Hadoop, Spark, Kafka, Cassandra, Tensorflow, Dataiku, Python.
Q. As you know there are many students in our program are interested in working as a data engineer, data scientist or software developer, can you give us some suggestions for how to land a job and develop a career in these areas? From your perspective, what skills do you recommend us to enhance apart from the courses?
A. For the technical round, I would suggest students stick to the basics, focus on core concepts before making a wide stride. Also, read a lot since this is ever evolving field, there is always something new information coming, so try to keep updated as much as possible. Most importantly, go through your resume a couple of times as the majority of the questions would be from your resume. Moreover, in job interviews most companies also ask behavioral questions apart from technical, so for this take help from SFU coop team (Eunice, Paula, and Laura) and book one on one meetings for mock interview sessions, resume and cover letter proofreading etc. Furthermore, reach out to senior cohort students if someone is/was working in the same company that you got an interview for.
Apart from the course curriculum, I would suggest students participate in Hackathons, Meetups, Kaggle competitions etc. in order to learn and gain experience.
Q. The majority of students in our program are international students, and some of them have foreign work experience. Since you have worked at IBM India and RBC Canada, do you think the culture difference between Canadian companies and the foreign ones would affect the way employees work? How can we adapt to these changes?
A. The major difference that I have observed is the team size, here in RBC the team sizes are small as compared to IBM India due to which there is a strong sense of ownership of whatever work you do and you have more accountability, whereas in IBM India this wasn’t the case due to larger team size the onus is mostly on the senior-most member of the team. Also, here in RBC we have regular brainstorming sessions where we discuss the different approaches to solve a given problem and everyone from a coop to director level employee give their inputs, on the other hand in IBM the use-case definition and the methodology that needs to be followed is provided by the senior/manager level colleagues.
So, all in all, in Canada the teamwork and participation are essential skills to become successful at the workplace, therefore, I will recommend international students to do more team projects over individual projects and get a firsthand experience which will help them to get adapt faster in Canadian companies.
Thank you so much for your time! We really enjoyed learning about your background, your thoughts about the roles and industry, and more importantly your advice for us. Good luck with your ongoing career！
About the authors: we are Denise Chen, Anton Ma, Angel Zhang, and Changsheng Yan from SFU Big Data Program. If you‘d like to know more about our program study, please leave a comment below to let us know your idea! Or you can check our program website at https://www.sfu.ca/computing/current-students/graduate-students/academic-programs/bigdata/about.html for more details.
