Episode 3: How to Get a Job in Data Science

Hello! And welcome to a new edition of the Data Science Now newsletter. In this session, I talked about how to get a job in data science, sharing my best tips and advice. You can hear the podcast version here:

And if you prefer you can watch the video recording here:

Remember that we will be live every Wednesday here at Linkedin, 8 PM CST :).

Getting a job in the field of data science can get you confused, some people say if you use excel you’re a data scientist, and it's not that simple. You need to have several skills.

I divided the session into 3 parts (you'll find a short version here):

1) What you need to know

2) How to prepare for an interview

3) Apply for the jobs and get calls

I'm basing this episode on two articles I wrote in March 2018 and January 2018. You can find the first one How to get a job in Data Science:

This one came up from 3 posts I did on LinkedIn, I talked about how to get a job in Data Science, I also have some articles on how I found my job, and my journey to Deep Learning.

There I explain how I applied to over 125 jobs, it was a lot of work, got like 20 replies, like 3 years ago. Some were thanks but no, I got 15 interviews, I got better and better, the first was awkward, I was nervous, I got better and had to deal with rejection, remember: don’t take it personally, maybe you're not a good fit, in the end, I loved the process. Now you get how much I did to get my job.

1. What you need to know.

When I started in Machine Learning and Data Science, in 2013, I started to care about ML, how to code in Python and more, I didn’t have a clear picture of what to learn, I didn’t have a plan, I was just interested.

After failing, I got on the right path, following the right people. Some of them will tell you you don’t need math, I think that’s wrong. Never trust anyone who tells you you’ll be an expert in months. If you want to understand all the insights, get a career, it can be engineering, computer science, physics, chemistry, biology, etc.

If you don’t want to get a career, there are ways to get the help of great people. You need to take courses, there are online courses, whole semesters from great universities on Youtube, but do the assignments.

Learning math is not only a tool, it can help you understand the world better, expand your mind.

You don't have to be an expert. You need to understand matrices, algebra, statistics, pay attention to it. Learn Python and R as well. Both for getting a job or your own projects. Also a little bit of C and Java. Also, the languages of the web: Javascript, HTML, CSS, they'll help you deal with web pages, scraping, etc. 

If you’re learning statistics do exercises, R and Python have great libraries for that. It’s a great combination.

Understand the basics of business, how to do a project, what’s a KPI, solving problems in the industry. 

Internships, get them, you can learn about business without full responsibility, deal with people, departments, projects.

Don’t worry if you feel lost, overwhelmed. Follow a good path and you'll get there.

2. Get an interview. How to prepare for an interview.

The truth is that some people recruiting don’t have the full picture of Data Science, they're not experts. If you're a recruiter listen carefully to the session, I have good ideas on what to ask the candidates.

You as an applicant shouldn't make them feel bad, it’s a new field, sometimes they’re headhunters just trying to find a good fit. Listen to the questions, if you don't understand something, ask them to say it again.

There are platforms like Linkedin, I've been getting my jobs here. You have thousands of jobs to apply for. Your resume is crucial to stand out. If it is for Data Science, have specific things, like your studies, experience, abilities. Have a portfolio in your resume, it's the projects you’ve worked for, demonstrate that you have worked in ML, explain in simple words how it helped the company. Depending on the company it's more or less important, but it's a big deal.

There are systems that scan your resume before getting to a human. Add keywords, read what they want and include the keywords, don’t lie. You need to pass that filter. Not every job is looking for a data scientist. Understand your responsibilities so you don't get frustrated if it's not what you want. Maybe they want a data analyst.

Don’t apply just because. I chose mine because I was interested, I wanted to work for these companies, in Linkedin you have filters, use them. You have an easy apply where you have to submit a PDF resume, phone number, and profile. They need to match to your LinkedIn profile. Make sure to have a 5-star profile. Fill everything. Always have a picture, your name not all in caps. You have a section to describe who you are. Put the keywords there too, recruiters can find them.

More places, sometimes LinkedIn will lead you to the webpage. Apply there carefully. You don't wanna have errors, have perfect spelling. Indeed is another page. Similar to LinkedIn. Some prefer that.

Other places: Stack overflow, job posts. Github jobs u can find jobs for data science, programming, a link to the page of the company.

Have something on Github. Put your projects there, I got calls from my Github profile alone. It's important.

For the technical interview, you can go and do a test, or it can be online. Also, there are systems like hacker-rank, where you can create a test, it's widely used so practice there.

They can give you a Data assessment, a simple project, say we "want you to do a presentation about your job". Take all your work and put them in some slides. Make sure you understand what they want from you.

If you apply and they reply, probably they're going to give you a short call. If they care about your profile, send your resume or cover letter. Maybe later you’ll have a call with the Data Science department, or someone related to Data Science. Normally the final part is a technical interview, some are just talking about how you would solve a problem.

Some do on-sites, where go to the company and spend some time there. Make sure you know the process and know if you can do it.

If you see my article The two sides of getting a job in Data Science:

I put recommendations from great people, you'll have the links in the article.

My final recommendations:

• Be patient. You’ll apply to a lot of jobs before getting one. Hopefully not that much.
• Prepare a lot, you'll be an important part of the company.
• Have a portfolio, do projects, post them on GitHub, apply for Kaggle competitions, write blogs, find something you love and problems and then solve it.
• The recruiter is your friend, they want you in the company. It's their passion. They wanna help you. Be able to tell them if you’re not comfortable.
• Ask what they do. If you wanna know what a data scientist does, ask what they do in a day, so you know if this is what you want to do with your life.

If you want an internship, have your academic skills on point. And explain what you’ve done. And how it can relate to the job.

Be active. It’s much easier to get a job if someone contacts you, make sure they see you, your work, etc. It’s the ideal case. Simpler to get the job. Don’t be afraid of talking, writing, creating videos, etc., so people notice what you do.

I think now is the best time to start learning data science. I’m launching courses with Closter, so make sure to follow us and subscribe to the newsletter for more info.

Always Remember:

There's no easy path, you have to practice, study, and if you want to know where you're going, you need to understand where you come from.

Thanks for reading this, please share this with your network, it would help us a lot :)

With love by the Closter Team:

Gabriel Erives, Héizel Vázquez, Eilén Vázquez, Favio Vázquez.