I work in data analytics and had this question asked by a fresh graduate: “You are a data analyst so why do you do machine learning?”
In his mind, it was really weird for me to put my title as a Data Analyst. He is after all the data scientist or machine learning engineer of a big e-Commerce startup. Two titles that currently holds prestige in data analytics today.
However, in Google, the title does not matter. It does not matter if you are a data analyst or software engineer. They would expect you to use machine learning if your daily job requires it. Even though I call myself an analyst, I still held a technical role. I detect phishing attacks with computer vision, image recognition, and machine learning from Gmail and Chrome as data sources.
In conventional definition, I would be a full-stack data scientist who extract data then generate insights/model with production maintainable code.
The conventional definition of Data Analysts and Data Scientists.
When I started my data science journey, I interned at Lazada as a Data Scientist. In Lazada, they have 2 types of data scientists:
- Data scientists who use excel or dashboard visualtion tool to generate charts and business insights.
- Data scientists who use machine learning and statistical rigour to analyse data and discover insights.
Both of them are called Data Scientists and work with data, but inherently the roles are different. By Skillfin’s definition, they would call number 1 as data analysts and number 2 as data scientists. Some startups call number 1 as junior data scientists. Of course, the latter is what you usually expect to work on as a data scientist.
Dirty Title Play
The abuse of the “Data Scientist” Titles
It seems that as the title of data scientists keeps evolving, it grows more and more saturated. 5 years ago in Singapore, the data scientist titles were not prominent. However, as the data trend became larger, many researchers, consultants, and educators jumped into the trends and took up the data scientist roles.
This has led many startups to abuse the title “data scientist“. As a result, the term data scientist has become more and more overused. Nowadays, we could see that some data scientists have rebranded themselves into Machine Learning Engineers or Research Scientists.
As humans, we are natural reductionists, we always want to simplify stuff so that it is much simpler to understand. Nowadays, many companies segregate their job description with the title data scientists. The core idea is that data analysts build dashboards and preliminary analysis then data scientists build machine learning models.
When I mentioned this to a colleague who moved from a startup to Google, He told me:
Just a little while ago, 10 of my colleagues changed their titles from data scientists to machine learning engineers. They then asked me, “How come you get demoted to a data analyst?.” To them, I just answered “I don’t care, I get paid more.”
The reason
Why does this title play happen? Simple, it is because the new title would attract more people and prestige. A lot of startups will tweak their titles and job descriptions to ride on the trends and have more fresh grads and professionals applying to their company.
Remember the fresh grad who approached me at the beginning of this story. He told me that he was really happy to change his title from data scientist to machine learning engineer. To him, it made him more valuable and worthy.
In the end, many got disappointed. Hiring managers were disappointed, they thought they hired a good data scientist but ended up with a guy who only knows how to implement the libraries without the reasons why. Their job description does not fit the title of the job.
On the other hand, the workers were also disappointed. I have a few friends who worked as Data Scientist Interns, but deeply frustrated as they only manually entered data labels.
From here, we learn that if you do not analyse the title carefully, it will bite you in the future.
What really matters: Solve your Hiring Manager Problem
Based on my interviews with multiple data scientists/professionals, what I found matters are only for you to focus on your experience and your skills. As an educator, I always tell my juniors: focus on the value you deliver.
Every interview comes with one simple goal in mind:
How do you solve your interviewer’s problem?
You will need to assess what assets you have which is valuable to you and your interviewers. No matter how much fluff and “resplendent” words you put to beef your resume, you will not succeed if you fail to answer “how do you solve your interviewer’s problem”.
In Google, the interviewers will test your previous experience and how you will fit and learn into the role. Each interviewer will give a hiring package to the hiring committee with an anonymous identity. That means the hiring committee does not know each interviewee’s name, sex, and even race. Then they would purely choose based on what the hiring package says to your merit. Your title will have a little say in this.
The interviews also will conduct structured interviews. Meaning that they would ask a lot of hypothetical (what if…) and behavioral (what have…). Both of these questions are paramount for Google to identify your value. I have more information about acing your data analytics interview that fits these descriptions.
Then after you get hired. This is a time where you need to focus on building your skills and excel. I have written about the importance of this from this article of mine.
Another reference is Deep Work by Cal Newport where the author teaches you the techniques to nurture your skills within your limited time so that you could learn and be valuable in today’s distracted world. From here, I learned to batch my work and get the focus to plunge in the complex world of data analytics and machine learning.
Conclusion: Focus on work rather than a title
In conclusion, you are not judged by title and fluff. But by the impacts of the work that you do. Solve your interviewer and hiring manager’s problems and you would be fine. Think about how you can build the necessary values and skills to prepare for the career ahead.
For me, I always use my free time to conduct my side projects and package them nicely to Github or other blogs. Then I would deploy these applications and write those so that it would bring value to my juniors and other data practitioners. Within a few months, my LinkedIn has become recommended my profile for Google HR to connect with me. He found my profile when he typed “Data Mining” and “Data analytics” on LinkedIn Search. What a wonderful surprise!
In summary, remember these actionable plans:
- Be wary about your Title and Job Description. It might not be what you are looking for.
- Focus on the value that you bring into your resume and interview.
- Excel by developing and contributing your skills. Then work deeply to contribute more values.
If you bring many values of your work to others, you would get many opportunities. This is how you should traverse in your career.
Finally…
I really hope this has been a great read and a source of inspiration for you to develop and innovate.
Please Comment out below for suggestions and feedback. Just like you, I am still learning how to become a better Data Scientist and Engineer. Please help me improve so that I could help you better with my subsequent article releases.
Thank you and Happy coding 🙂
