One of the most common questions that aspiring data scientists ask is – ‘how do I get a data science job?’ There are many professionals looking to transition to data science but don’t know how. Therefore, this blog explains how you can get a data science job.
What to Know Before Applying
I want to make one thing clear at the start – getting a data science job is not easy. Sure, there are scores of openings and many companies are looking to hire data scientists so that they can gain an edge over their competitors using data. But, they aren’t simply hiring any Tom, Dick and Harry. So before applying for a data science job, you need to know that you must be patient.
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Most data scientists, who have written about how to get a data science job, have said this. The reason? They all had to apply to over 100 openings (sometimes even over 400!) before getting the 1 or 2 that really fit. If you want to get a data science job, be prepared to send in many applications and don’t be disheartened if you don’t get a response from every recruiter.
When I say prepare, I don’t just mean prepare. I mean prepare like your life depends on it because it does! Take your career interests very seriously and realize that anyone looking to hire you expects you to be an important part of their organization as a data scientist.
This means they expect to see problem-solving ability, adaptability to different work situations, good communication skills to collaborate with a variety of stake holders, and obviously, other – technical – data science skills that are necessary. So, prepare yourself well to answer some tough questions.
Work on yourself and have a growth mentality. Talk regularly about data science with people in the community. Attend events and seminars that will help you to learn from and network with professionals in the industry. And finally, find out who you are as a data scientist. Know what skills you possess, how good they are, what data science job roles they suit best, and where you want to work.
Developing a Portfolio
If you want a job that pays, you need a portfolio. Not just any portfolio. A portfolio that demonstrates real-world experience in problem solving. It should include projects that demonstrate how you used data science to help achieve a business’ or organization’s goals.
Think of your portfolio as a chance to show the employer that you can analyze a situation and identify objectives and use data to achieve best possible results. This does not have to be for a client or a company, mind you. It could very well be projects that you worked on independently, or for the data science community.
Skills You Must Demonstrate
- Cleaning data: the ability to transform data from a messy state to a usable one for actionable insights.
- Analyzing data: show how you were able to draw actionable insights that were otherwise unknown or hidden.
- Storytelling: data science is all about communication. Show the employer how you use narratives to communicate insights, collaborate with team mates, and achieve results.
- Visualizing data: the stories you that tell with data are arguably most effective when they are visual – when they leave impressions on the retinas of the people, who are engaging with it. Visualizing data is an important skill for any data scientist. It helps people remember and understand your data more easily.
- Machine learning skills: one of the most in-demand set of skills. If you possess them, there is no reason for you to hide them.
- Writing: I’ve written before that this is one of the most important skills a data scientist can possess. Aspiring data scientists, who think their work ends after writing the code or completing the assignment are utterly mistaken.
Be sure to highlight how you used these skills in projects well. Always assume that you are communicating with at least two sets of professionals in the hiring process – technical and non-technical. Chances are that the non-technical professionals will go through your profile first to see if you meet the basic requirements. Therefore, communicate to both sets of these parties and include all links that may be relevant so that they can check them.
Making Your Resume
Hiring managers are most likely to find your projects through your resume. Here are a few things to keep in mind while crafting it:
- Keep it short.
- Be accurate.
- Include relevant training and coursework.
- Highlight skills used in projects.
- Include your profiles on GitHub, Kaggle, etc.
- Show how you engage with the community by highlighting contributions and knowledge sharing activity.
- Don’t assume you can use a standard one-size resume. Speak to the hiring manager about the specifics of the job, if you want more details to customize your resume according to the open position in their organization.
- Include a cover letter that explains your interest in the position and what you hope to achieve for yourself and the employer by taking it up.
Choosing a Workplace
The ideal workplace, especially if you’re starting out, is one where you will get to learn a great deal. Would a multi-national provide the right environment to improve yourself? Probably not, given how busy their employees generally are.
A small startup, on the other hand, may not have much experience in their data science team that you can benefit from. Look for a mid-size company where you can work with senior data scientists, who will be willing to mentor you. A mentor goes a long way in data science.
Getting the Data Science Job
There are three parts to a typical hiring process. First, the hiring manager or HR calls to verify your experience and screen you. This call is followed by a more technical telephone interview with probably a data scientist, who will ask you more in-depth questions to see how well you know data science. The data scientist could also ask you to work on a task or assignment after this call.
Finally, there is a personal interview to make sure you’re the best fit for their ambitions. Be sure to identify the question or problem in your technical and personal interviews and explain how you can answer/solve it using the data available and you will be most likely hired.
I would say don’t apply for your dream job, until you’re confident about attending data science interviews. Attend a few interviews to get a sense of the questions employers ask, and how to best answer them. When you feel confident about acing an interview, apply for the job that you truly desire.
I hope this blog helps you get the data science job you want. If you do, share your success story and let us know. You may also share any other tips that helped you to get a data science job by commenting below. Thank you for reading and we wish you all the best in your job search.