3 Things I Wish I Knew Before Starting A Career In Data Science
Data science is among the highest-paying and fastest-growing fields in the world. It is an exciting and rewarding career path many are considering nowadays. However, it can be challenging and nerve-wracking to enter a relatively new field, particularly for fresh graduates.
Many are confused about how they can land a job as a data scientist with just their degrees and whether they need to do more to prepare themselves for the field. Massimo Belloni, Senior Machine Learning Engineer at Bumble, shares the three important things he wishes he knew before starting his career in data science when speaking at ELU Tech Summit 2022.
Here are the three things to ensure a successful career as a data scientist:
1. Not Much Different Than a University Project
It can be stressful for fresh graduates to enter data science, a relatively new field. Fortunately, Belloni highlighted that having a data science project in your professional life is not much different than your university project, except you don’t have to deal with exams.
The theoretical background learned at the university is more than enough for any problems you must solve in your professional data science projects. Even in interviews, they ask you questions related to your theoretical foundation.
It is important to remember that fresh graduates have more information regarding data science than most industry workers. This is because accomplished leaders teach data science courses that help the students stay more current about the field.
2. Prioritise Getting Things Done Instead of Beating SOTA (Self-Imposed Target Average)
It is essential to understand that the complexity of the model is not a shortcut to success. You must focus on getting things done and finishing the project successfully and efficiently. Belloni suggests proving the business model first and then iterating.
Having the right mindset can help you revolutionise how things are done and have a massive impact on the overall business. Once you have completed the basics, you can always research and look for a more complicated framework. But in the beginning, if you are working in a company that doesn’t have a long history in data science, focus more on providing value.
3. Learning Dev-Ops Can Greatly Benefit You
Belloni explains, “When I used to work in smaller companies where data science is not mature enough, it was more complicated to get the models from the engineering team and put them into production.” He recommends learning the basics of DevOps as it can make a massive difference.
Deploying models autonomously to production can be one of a data scientist’s greatest strengths. To be impactful for your company, you should be able to deploy production and machine learning models by yourself. It helps speed things up and allows you to gain recognition in your company.
Focusing on making an impact and solving things can help you gain your company’s trust, particularly where data science is still beginning. As you work toward resolving the data science problems, you will gain more experience and will be able to stay current in your field.