Fireside Chat with a Data Scientist: Part I
Answers to some popular Qs on breaking into Data Science for pivoting mid-career professionals
This article is the 1st part of a 2-part read series. Once you've read this, don't miss to read the 2nd part (coming soon!) that continues to uncover answers to more burning questions around Data Science that are especially helpful if you're unsure about this field! These articles collectively curate responses from a Data Scientist's lens to some of the most commonly asked Qs that I've answered in fireside chat conversations. I hope this helps shed some light and provide clarity for readers, especially for those of you who are aspiring Data Scientists out there and enable you to make informed decisions before walking into the world of Data Science.
This part focuses on answering questions related to a Data Scientist's daily job scope, team, and journey. While part II focuses on uncovering answers to questions around hiring for and getting hired as a Data Scientist.
Let's begin!
What is a day like, in the life of a Data Scientist?
Data Science is a very Research and Development (R&D) heavy role. No wonder those graduating with PhDs are always cream of the crop hires! Everyday tasks could typically involve researching algorithms by reading technical papers and trying out the open-sourced code on your organisation's data. Running experiments, tracking the results and presenting in weekly meetings for progress tracking and roadblocks surfacing would also be part of corporate expectations. The highly experimental nature of the job requires heavy use of jupyter notebooks and any type of MLFLOW tools to track machine learning experiments rune. All written code is synced up to Github repositories to allow sharing within teams for reuse and ensure none of your hardwork on code development is lost.
What has the journey been like in this field since the initial start?
I never lie or paint a false story by saying that Data Science is easy! It isn't, it's a mountain trek to the steepest one you can think of, so yes, it's challenging, extremely! But with a growth mindset, the right support system, you just gotta keep pushing through. Magic happens along the way, like every code error you debug would feel HUGE! Because it would enable you to get one step closer to your machine learning goal, closer to predicting a churn, a price, a sentiment, and so on. That's exactly how it's been for me as well. As you grow in your Data Science career, what you classify as accomplishment will change, given your stronger expertise, and you'll soon be able to look back and pat yourselves on the back looking at how much growth this career has bestowed on you, from a technical lens.
I say this confidently because I for one, didn’t major in computer science, didn’t have a career in IT, but the destination in mind was clear, to shape my career into a Data-centric one, so I trained myself, one tool after another and not just stop there, also finding apt applications to "use" those technology tools to actually solve business problems and provide solutions like an automated business process, a user friendly impactful KPI dashboard, a churn prediction, and so on.
What kind of teams does a Data Scientist typically sit within?
Seriously, consider yourself really lucky if you're a Data Scientist within a full-fledged team like Product Development, where your peers are Data Engineers, Data Analysts, alongside other Data Scientists because you'll have such a healthy network of technical experts to learn from. In more specialized settings like mine, where I focus on People Analytics, I'm typically working in an Individual Contributor setting as the only SME working to drive Data Science solutions in not-so-technical spaces. Both kind of settings have their pros and cons. Basically, you're either a big-fish in a small-pond or a small-fish in a big-pond. The choice you make will define whether you learn more or have full autonomy.
What technologies and tools are most commonly used on the job?
Bread-and-Butter: Programming language like Python to pretty much handle anything from automation, reporting to advanced analytics — descriptive/predictive, anything pretty much.
Project Management: An Agile framework would typically be used to organize your team's work and projects with 2 weeks being one sprint. In every sprint, each team member has tickets related to your project to work on. A tool like JIRA would be used for ticket management (a ticket is basically a task assigned to a person). So each day, the Data Scientist/team logs into JIRA, takes a look at the pending tickets and use that to guide what they should work on at any given time.
While this article covered some basic questions around a Data Scientist’s role, team, and tools, make sure to keep a lookout for part 2 that’ll shed light on questions related to hiring/getting hired as a Data Scientist. Happy reading!
