If you are beginning your career as a data scientist, this article could give you a general idea of what to expect from the first job. There are factors to consider when starting a data science job, like whether a company uses machine learning or business analytics; the kind of tools the company uses for data science/analytics, or finally whether it is a large company or a small startup. Such variables are important in making the initial decisions on what to learn and where to focus on to advance your career.
When beginning a fresh job, whether you are a new hire or an experienced professional, try to lower your expectations initially and then slowly adjust to the work pace and processes. But this is true for any job role in any sector, and not specific to data science, or IT.
You May Have To Learn A Lot In The First Year or Two
As a junior data scientist, you may have stronger skills in statistics than in programming, and therefore, you may need a lot of learning in the first few years of your career. Even if you understand machine learning statistics and Python, you may still need to expand your knowledge in tools and libraries such as containers, PyTorch, Keras and further improve programming.
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The general suggestion is to learn Python libraries when it comes to intro to data science and data analytics are pandas and matplotlib. You can look for different examples and problems on StackOverflow and see how experienced professionals solve those problems.
In many companies, you may have to learn enterprise-focused programming languages like SQL or SAS for analytics/business intelligence projects. You can use MOOCs platforms like Coursera, edX and others to keep learning as a data science fresher.
Be Ready For Tedious Data Science Tasks Not Fancy Model Development
At the beginning of any career, you don’t jump right into the handling and leading the critical tasks, be it any profession. Similarly, data scientists starting out their career may also have to be in that position, where they may have to work on tedious and unimportant tasks, in the beginning, leaving the meaningful functions to seniors. At the same time, those tasks can help build a strong base for the future.
There will be more requirements for boring tasks rather than for very interesting cutting-edge work on machine learning. We know that behind all the advanced research work in data science, there will always be people needed to manage and do the tedious work.
As a junior data science professional, you could be busy with a lot of work, much of which is not interesting, and you would have zero time for additional learning beyond a certain point. While you can do your best to extract the data science skills from such tasks, it could certainly be mind-numbing at times. Sourcing, and cleaning the data is central to giving a business the necessary insights to learn how to use it accurately in research and building a model. And this could be the first major step towards being an expert.
Getting In Tune With Work Culture
Moving on, as a fresher in data science, if you do enough to get your hand on interesting and challenging AI/ML projects, it can make such a huge difference in your career growth. You will need to do a lot of data wrangling, EDA, develop a model and translate into results. But, it also needs a good manager who is helping you learn and letting you express your technical skills fully.
In some cases, you can find yourself a technology manager who may have the same amount of knowledge in data science as you. This could be challenging as you may not have anyone to turn for certain problems and help you through the process.
Before opting for a job, make sure to research the company and their culture. Make sure to ask them what kind of projects you are going to work with and how your everyday-life is going to look like. Try to choose a company that has good enough management and seniors which can properly mentor you and help you grow in the direction that you want. It is not a bad thing to discuss this during your interview process and ask them if they help with mentoring.