Data Science is probably one of the fields that people come into it from all sorts of backgrounds and education levels. If you ask 10 Data Scientists about their background and their prior experiences before landing on their first Data Science job, you will probably get 10 different answers!
However, regardless of your background, there are 5 common steps between you and your first Data Scientist job. Being aware of these steps and improving each one of them helped me and many of the people I worked with to land on our first job faster and easier.
Below are the 5 steps between you and your first Data Scientist job in the modern job market. Despite all the fast changes in today's job market, I can hardly imagine these 5 steps going anywhere soon. It is important to know these 5 steps and get good at each one.
1- Technical Skills
You need to learn the required technical skills one way or another. What are those skills and what's the easiest way to learn them?
Let me answer the second question, first. There are many ways to develop your Data Scientist skillset. I learned them in a hybrid way: I learned some of them through formal academic courses in college and grad school and some through online courses and self-study. Nowadays, there are also other ways such as Data Science Bootcamps and 1-on-1 Mentorship Programs. The best way for you might not be the same as for others. You have to find your best way. A good mentor can also help you find the best way to develop your skillset for your first Data-Science job.
What are the required skills for Data Science exactly? Python, R, and SQL are the top 3 in-demand skills in today's job market and there are many more. Although the tools may vary in different business sectors and may also change slightly overtime, its skeleton would remain the same which includes:
- Statistics & Machine Learning algorithms and the math behind them. Some naïve people think they don't need to understand the conceptual math and statistics behind the algorithms, but any experienced hiring manager would know the difference very well. You need to understand the logics of what's happening behind the scenes to be able to build anything useful and specially when something goes wrong (which happens constantly during model development).
- A Programming Language that gives you the power to communicate with computers to perform what you need them to do. If you know all the details and logics of Machine Learning algorithms and Statistical methods but you are not comfortable implementing those ideas through a programming language, you are not going to go far in the job market. Many programming languages have been developed over time for different purposes including R, Python, Spark, Linux, Bash Script, SQL, MATLAB, C++, JavaScript, and Rust. You don't need to know all of them to be a good Data Scientist. You only need to know one of them well, at least.
In addition to Machine Learning and Programming Language, it would be a big advantage if you already have Domain Knowledge in a specific field as well. For example, imagine two candidates applying for a Data Scientist position in the financial sector. Both of them are at the same level in terms of their knowledge in Machine Learning and Programming. But one of them has worked for a bank for two years. Which one would you hire? Domain Knowledge is not required to enter into a new business sector as a Data-Scientist (in fact you can learn that on the job over time); but having relevant domain knowledge certainly makes you more desirable to the employers.
I have seen some people thinking that developing the technical skills would be all you need to get you a job as a Data Scientist and I have to break the news that nope, it doesn't! Technical skills in Programming and Machine Learning is of course needed but not sufficient to get a job. You have to go through other stages too. The good news is that after you develop the technical skills, the rest is going to be much easier. By completing the skill development you are half way through!
2- Recent Projects
You need to demonstrate your skills in action. You read Data Science books, go to Data Science classes, and attend Data Science Bootcamps and still unable to perform a Data Science project end-to-end. Projects are a great way to demonstrate your skills in action. But why employers would like to see a recent Data Science project from you?
Put yourself in the shoes of a hiring manager! From his/her point of view, I want to make sure the person I am hiring is capable of doing the requirements of the job. If you are applying for your first Data Science job, a project is a must!
If your background is not a quantitative field such as engineering, math, or physics this becomes even more important to demonstrate your skills in action. First job is always tricky for both hiring managers and the candidates. You need experience to get a job, and you need a job to gain experience! You need to break this loop and doing a project is a great way to achieve that.
If you don't know what project you should do and to what extend you should work on it, an experienced mentor can help you with that. Working with a professional, experienced mentor is a good idea and can help you save a lot of time guiding you from where you are to where you want to be with less pain.
3- Resume Review
It is crucial to position yourself in the job market correctly. How your one-page resume can help you do that?
Your resume is your ticket to get your foot in the door. Your professional network and your personal connections who already know you might not need to see your resume. The reality is, most of the people are outside your network and if you are looking for new opportunities outside of your network, you need to be able to communicate who you are and what value you offer quickly and efficiently. That is called a one-page resume.
I've seen people with great experiences who cannot get the attention they deserve in the job market just because of a poorly written resume.
It is important to make sure your resume is simple, clear, and tells a unified story about your professional experiences. This puts you in a sweet position and the employers want to talk to you through a job interview after reading it.
If you need professional help on writing or shaping your resume, reach out to professionals and spend a lot of attention on it. It is a great investment of your attention. It will pay off in the future and will stay with you for a long time.
4- Job Search
With a good resume in hand that efficiently communicates your developed technical skills and projects, now it is finally the time for searching and applying for your dream jobs.
Job searching is a process. It is going to take you some time. Having a strategy for this process can help you reach better results and reduce the time of the process.
Below are a couple of quick tips and tricks that can help you develop a better strategy for your job search.
- Search/Apply in batches. What do I mean by that? Send a bunch of applications for a bunch of roles that interests you, at the same period of time. Don't wait for one application to take action on another. Monitor and take action on your batches through the process as needed.
- Narrow Down Your Search. It is a wild world out there in the job market, know what you are looking for. Be clear about your criteria and focus on your interests. You could narrow down your search by your preferences on field of experience and opportunities, geographical location, salary range, business sector, company size/culture, etc.
5- Job Interview
You've got a job interview; Congratulations! You have done the hard work so far, but it is not done yet. At the very final stage before you land on your first job you need to go through a job interview. It is the time for you to communicate yourself and your abilities.
Job interview is a process. It is going to take you some time. Having a strategy for this process can help you reach better results and reduce the time of the process.
Below are a couple of quick tips and trick that can help you develop a better strategy for your job interview:
- Company interviews you and you interview the company. Do not accept to work anywhere that offers you a job. Be cautious and try to learn as much as you can about the team, the hiring manager, the company, and the role during the interview. Interview stage is usually the stage in which companies treat you to the best of their abilities. So if you do not like the way they treat you at this stage, don't expect any better on the job for years to come.
- Learn as You Search. There is a lot of learning in the process of job interviews as you go through it. You are going to get better at this as you go. Use the lessons learned from the previous interviews to improve the next ones.
Conclusion
Data Science jobs are still in demand. There is still a big shortage of Data Scientists in the tech industry in various sectors. Thousands of people with various backgrounds are getting into the field of Data Science every year to help fill the gap.
Regardless of your background, there are 5 major steps between you and your dream job as a non Data Scientist to a Data Scientist: (1) Developing the required technical skills specially on Programming and Machine Learning, (2) Demonstrating your skillset through end-to-end Data Science projects, (3) Resume Review to make sure you position yourself properly in the job market based on your skills and your interests, (4) Job Search and (5) Job Interview.