First of all, congratulations on landing your junior data scientist role. You have finally got your foot in the door leading to the world’s most coveted job and setting yourself on a data science career. As with any job, your first year at the job can be overwhelming, when you understand that there’s so much to accomplish. But fear not, knowing what’s coming your way is a relief.
So here a few things that you must do —
1. Keep your expectations lower. Ideally, we set our expectations from a job high, which if not met, demotivates us. Keep your expectations lower and try to adjust to the process at work.
2. See what tools your company is using for data science and analytics
3. Check whether the company’s focus is on machine learning or business analytics
4. Learn whether a company is large or small
These factors will play a crucial role in understanding where to focus to advance in your career.
Make your first two years about learning
As a junior data scientist, your primary strength area is statistics. The hiring decision for junior roles in data science is based on knowledge of statistics unless the role description specifies proficiency in visualization tools or demands other niche skills.
In the new role, you will be expected to become better at programming. Even though you are good at statistics, you will understand to Python, you will need to broaden your knowledge in tools and libraries such as containers, PyTorch, Keras, and further improve programming. Working on projects outside is a good idea.
A rule of thumb is to learn Python libraries extensively. Pandas and Matplotlib are core packages for data science and analytics that you should pay attention to the most. Kaggle, Stackoverflow, and Data-Driven are a few places where you can learn from experienced data science professionals to solve problems that you find hard to solve.
Most enterprises use R for large scale data analytics projects. So you will need to get your hands on that too. You can take data science certifications like ABDA (Associate Big Data Analyst) from DASCA, Dell EMC Proven Professional – Data Science Associate, etc. to keep learning new skills in a fresher data scientist role.
Get ready for messy data science tasks, forget model development
As you start a junior role, don’t expect to jump straight to building models. In the beginning, you will need to get your hands dirty by doing some tedious tasks. This is common to all professions, not just data science. Junior data scientists work on cleaning data obtained from various data sources. This is perhaps the messiest task to do as a data science professional.
The tasks you do as a junior data scientist may seem irrelevant sometimes but they are extremely to reach the final stage – building a model.
Interestingly, there is more requirement for boring tasks rather than interesting work or machine learning. However, you must not forget that that behind all great work in data science, there are junior data scientists who manage and do tedious tasks.
As a junior data science professional, you will be busy with a lot of tasks. At times, you won’t find time for additional learning. Try to extract as much data science skills as you can from the tasks you do at your job. The tasks can be mind-numbing at times. Collecting and cleaning data is a core task to discover insights for businesses to act and improve and optimize their processes. This task is important for further research and building a model. This takes you toward becoming an expert data scientist.
Taking short-time data science certifications or courses to build on your existing knowledge will be helpful. Participating in open source data projects will also be helpful if you can manage to do so.
Embrace the company culture
You will be working on some tedious task, but this is not to say that you won’t get to do interesting tasks. If you do well in the tasks allotted to you, companies will be quick to give you challenging AI/ ML projects. As part of these projects, you will need to do a lot of data wrangling, exploratory data analysis, develop a model, and translate into results. To do these projects effectively, you will need a good manager who helps you understand the objectives of the project and guides on how to do it.
You may find a technology manager who has the same amount of knowledge about data science as you. This could be challenging for you as you won’t have anyone to turn to in times of challenges.
So before choosing a job ensure that you research the company and its culture. Make sure to find out the projects you will work on. Try to choose a company that has experienced data scientists and good management, and senior professionals who can help you learn and grow in the direction that you want. Discuss the possibility of mentoring on the job.
