We have heard so much about the glamorous side of being a data scientist. After all, it was named the sexiest job of the 21st century.

However, have you wondered what really goes on in the day to day job of a data scientist?

For this session, we talked to Dara Tumenbayeva – a data scientist at SEEKAsia (parent company of Jobstreet), and also a serial hackathon enthusiast, to share her journey and aspirations as a passionate data scientist.

Tell us about your journey in becoming a data scientist.

I am a data scientist at SEEKAsia, and I am from Kazakhstan, and I was working initially as a software engineer, before eventually becoming a data scientist at SEEK Asia.

Dara shares industry advice for aspiring data scientists in this interview.

What drives you to become a serial hackathon enthusiast?

There is something fun about hackathons, which is how you are able to be in touch with innovation. This is why you find me frequently attending hackathons whenever time allows me to.

As a data scientist, in your day to day work, your responsibilities may get repetitive or even boring.

A great way to occasionally jump out of that is to attend hackathons,  where I found it has always made me more creative and innovative – helping me to open up to new learnings and experiences.

Dara is a hackathon-star who wins almost every hackathon she participates in.

What is your best experience in a hackathon?

One of the best experience I had has to be the one in AT&T Hackathon, where I got a chance to win an Udacity scholarship, and I chose the machine learning course. I am currently working through that course as well.

Do share with us “A Day In A Life” of Dara, the data scientist.

Well just like many people, in the morning I go into work, get my coffee, etc.

However, my day to day really depends on what I have to do that day. There are days where it would be more focused on meetings and planning, which is really crucial for a project.

But other than that, I do a lot of data related work, such as cleaning the data, understanding and figuring out how to organize it and translating it to a way that makes sense to our stakeholders.

With all data-related projects, you will need to first solve a business problem, right?

Yes, usually before a project, we go through quite a few meetings with stakeholders, to understand what are the requirements, what are the problems behind it, what is the value it brings if we were to solve it.

Only after these discussions, the actual work of data processing and analysis comes in.

How long do these data projects usually take?

The period depends on the project. However, it is often never-ending. As a data scientist, you will realize you will take much longer to complete the tasks, say, in comparison with software engineers.

It’s hard to put a stop to a project as it is usually never-ending. After Phase 1, it usually continues to Phase 2, Phase 3 etc.

So I would advise anybody planning on being a data scientist to be someone who is constantly enthusiastic about solving never-ending questions and problems. You basically become a problem solver for some of the most challenging business questions.

What are the important business questions you will need to ask before kickstarting a data project?

The most important question is how much value the project will bring to the company. We want to make sure that it is worth it to work on a specific project before getting started. This question can be either answered by the data science team or the stakeholders.

The next thing we need to explore is the resources available, to see if we are able to accommodate the project.

How do you get through the boring period of data cleaning and modeling?

Just like any other professions, there are going to be some parts of your job that can get boring. But what keeps me going is knowing that there will be more exciting parts coming up after the boring, but much-needed processes, and we just have to power through it.

It’s important to know that there is always a less glamorous side to all jobs, and boring parts are just part of the job, and you just need to get it done, one way or another.

Can you share with us an example of how SEEK Asia use data for the success of the company?

There is this project we are currently working on, in which we are doing candidate segmentation, whereby we try to understand among active users, how likely are they responding to different ads, how to optimize our targeting towards them.

For example, if a user is less active, we will send them promotional emails on a weekly basis, as compared to the daily emails we send to much more active users.

So you have to work closely with the Marketing team. How do you communicate your findings with them?

This is actually quite a challenge the data science team has to face, which is to communicate and explain our findings to other teams in the company. However, it is very crucial we do so, so we have to find ways to make it work.

An effective way is to simplify the findings to its most basic terms so that they can understand. Using jargons and overloading them with information will be intimidating for teams who do not have a technical background. So it’s important to breakdown and present the findings in a way in which they are able to relate.

To make it even simpler, we create visualisations like graphs and simplify our text to actionable terms, so that the other teams can directly make tweaks and changes according to our findings to optimise the results.

What is your advice to advice to someone who is looking to venture into data science?

Just to sidetrack, through our company’s data insights, we found that most employees are confused as to categorizing data science-related job openings on our platform.

So I guess its important for new data science job seekers to first understand the differences between the roles and responsibilities available.

Here is a simple breakdown:

  • Data Engineers: You will be working more with actual codes and data, such as
    organizing and cleaning the data.
  • Data Scientists: You will be working more on modelling, in which you will consume the organised data from data engineers and work from there.
  • Data Analysts: Your main responsibility is to make sense of the results that data engineers and data science has produced.

What is the ideal future for your career development in the data science industry?

Based on my current journey, I would want to venture into management roles. Even in my experience in hackathons, I find that I have better synergy when I am leading a team, and that is when I perform the best.

I enjoy knowing the end goal and leading a team towards it. So an ideal situation is if I am leading a team to build Machine Learning tools, whereby if necessary, I am able to step in and do it.

But most of the time, I want to be working on allocating the resources and understand how we can build the app so the client can get the best value.

Dara is a passionate data science enthusiast, who is continuously pursuing knowledge in the industry.

Learn more about setting up your own data science journey and discover how data science is being applied at companies like SEEKAsia in D/M Summit. Dara shares a topic on the pitfalls of data science at work during the summit.