A career in data science can be exciting but it also comes with its fair share of challenges. In this highly competitive data science industry, you need to devise an effective plan to get hired — more than just complex projects and a strong basic foundation. To decode such strategies, we as a part of our weekly column called ‘My Journey In Data Science’, interviewed Imaad Mohamed Khan. He is a data scientist and a serial entrepreneur for the past few years.
The Background
Khan is based out of Bengaluru, where he completed his schooling and graduation from MSRIT in Electronics and Communication Engineering. He then went on to pursue his masters in Internet Technologies and Information Systems from TU Braunschweig, Germany.
However, his journey in data science commenced only when he took an online course on Coursera in 2014. While talking about his love for data science, Khan stressed on his proficiency in solving mathematical problems in school, and the programming skills that he acquired in his college days. “Data science was natural as it is a mix of problem-solving, mathematics, and programming skills,” says Khan.
Although data science seemed an obvious career choice because of his various capabilities, he had to struggle when he moved forward with his masters to gain knowledge in the computer science domain. “It was an arduous task as I did not come from a computer science background. I had to double down and improve domain skills,” mentioned Khan. “During my time in Germany, I worked on my research project, and my master thesis was machine learning projects, helping me obtain an understanding of the data science field,” he added.
Strategy To Get A Job
Like any aspiring data scientists, initially, Khan was optimistic to get as he had a master’s degree, a wide range of ML projects, data intuition skills, among others. However, it wasn’t to be — he had to deal with rejections and self-doubts. Consequently, he devised different strategies to get offers, and he suggests other aspirants in order to get numerous offers. His plan was to tackle the following:
- Search on job portals
- Write blog posts and personal project
- References
- GitHub
- StackOverflow
- Kaggle and Hackathons
- Attend meetups
While Khan cited how just like others, he started by applying through job portals, which he says, is the most difficult way. “Unfortunately, most of the aspirants focus mostly on applying through job portals. Rather, they should diversify their job hunt by generating content related to data science projects, ask for referrals, find jobs on LinkedIn,” explains Khan. “I implemented those and got at least one offer from each strategy,” he added
Besides, he also described some effective ways that his friends and colleagues embraced to get a job: Creating a great profile on GitHub and StackOverflow, competing on Kaggle and Hackathons, and attending meetups.
While there could be many ways, the aforementioned are effective ones and can work for most of the aspirants.
Surviving In The Competitive Data Science World
When asked about the strategy to survive in the massive data science ecosystem, Khan depicted that he had to convince himself that the data science landscape is enormous and cannot be an expert on every technique, but he always remains open to learning new methodologies as and when required. Khan says, as the technologies are moving fast it is crucial to keep learning throughout the course of the journey. He took this inspiration from Andrew Ng, who continually strives to enhance his skills.
Such clarity in thoughts has led Khan to assist his company in delivering new products. More notably, in an internal hackathon, he implemented a search engine that was placed in production. On the other hand, among the most difficult experiences, Khan said he finds it difficult to convince non-technical people on ML and AI cannot do, at least at this point of time.
Guidance For Budding Data Scientists
“Engaging with the data science community is of paramount importance. This helps in learning from diverse people from around the world. Therefore, it increases knowledge as one can be exposed to various perspectives. My advice to aspiring data scientist would be to be open to learning new things,” concludes Khan.
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