Samanvay Karambhe - Why I became a Data Scientist

I’m Samanvay, a data scientist from Sydney, Australia. After graduating from high school, I had a clear vision of what I wanted to do. It was to design and build satellites for a living. You can imagine my excitement when I got accepted into the Bachelor of Aeronautical Engineering (Space) course at the University of Sydney. Overall it was an enjoyable, albeit very challenging, degree, which allowed me to work on a satellite that went to space, intern at the German Space Agency and build VR systems for space exploration. 

 

What inspired me to get into Data Science?

At the end of my third year, I stumbled across The Human Face of Big Data. It would be an understatement if I said that the documentary changed me. It was my first introduction to the fascinating world of data science, and this one particular story from the feature stuck with me. During the Haiti earthquakes, the affected population started tweeting wreckage locations to aid emergency services. A group of voluntary data scientists got together in Boston and decided to create a geographical heatmap by mining these tweets, and as a result, this expedited the on-ground rescue efforts conducted by rescue services. 

A realisation dawned upon me that data scientists had the ability to make global change with just a laptop and data analysis ability. Given that we produce 1.145 trillion MB per day, there is plenty of need for data professionals in the world. 

While learning about data science, I quickly realised that aerospace engineering was quite the opposite. To me, it felt niche and restrictive impeding my ability to work in multiple industries and on a variety of problems. Not to mention, the tech landscape was rapidly changing and highly dynamic. 

Ultimately, the decision to enter the field of data science wasn’t difficult. Having now spent 5 years in the field, I couldn’t recommend it highly enough, especially if you love problem-solving and dealing with ambiguous data challenges. So far I’ve worked at WooliesX, Nearmap, and Lexer working with retail data, financial data, customer data, and aerial imagery data. 

 

How the pandemic has affected me in my work as a Data Scientist.

This pandemic period has been especially eye-opening in realising how grateful I am to be working in the software domain. The role of data science is to build efficient systems to optimise various processes; this meant that the majority of data professionals were shielded from the prospect of redundancy during the pandemic period. In fact, the IT industry was actually booming during the period due to software related jobs being unaffected from working from home practices. 

 

The best thing about being a Data Scientist.

Apart from job security, the best thing I love about being a data scientist is learning and practicing a blend of technical skills and soft skills. Not only do you build data products, but you also get the opportunity in dealing with stakeholders and clients. There aren’t many other professions out there that offer the ability to sharpen your leadership skills as well as your technical skills simultaneously.

 

Advice for Data Aspirants.

There hasn’t been a better time to get into data science. If you’re just starting out, focus on a holistic approach to learning, which includes learning Python programming, ML principles, and statistics. Try to build personal projects that involve using those skills, as this serves as your portfolio for applying to jobs. 

In the learning phase of data science, it’s quite easy to get overwhelmed with the broad range of things you need to know. Don’t be scared, it takes years to learn the different aspects of it. Be patient. Your learning will never stop. 

The domain of data science is so fast-moving and it can be quite a struggle to keep up to date with the latest trends. For me, the easiest way to stay on top of the trends is to follow key players in the industry on LinkedIn and to sign up to The Batch, which is a weekly AI newsletter by Andrew Ng’s company Deeplearning.AI.

 

A common misconception about Data Science.

A common misconception of being a data scientist is that you will always be working with fancy models. In reality, that’s only 10-20% of your job, and most models you’ll use won’t be that fancy. For the rest of it, you will be building systems around the model and dealing with stakeholders. It is not like a software engineering job where you spend the majority of your day coding. 

 

My Top 3 dinner guests!

Richard Feynman - He was someone who found many things interesting. He didn’t shy away from becoming proficient in things that were left field to his array of expertise, Physics. He learnt to be a proficient lockpicker and an artist. He was driven by immense curiosity which is very inspiring to me.


My Grade 4 Teacher - He was the person who instilled a love for reading and being curious in me — two qualities that have significantly shaped how I view learning and living. I would love to meet him again at this age to learn from more of his wisdom. 


Sylvia Plath -  She was a tremendously gifted poet who lived a life of turbulence and misfortune. I would love to meet her to learn more about her from a human perspective. I have long been a fan of her novel The Bell Jar.

Follow Samanvay on YouTube, LinkedIn, Twitter.

 


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