How Do I Get My First Data Science Job?

The advantage, of living at a time when technology and social media are accessible, is that there are so many opportunities to showcase your portfolio. Here are the aspects of your portfolio that you have direct control over and which you can use to represent you and your work, hence your "brand":

Resume
LinkedIn profile
GitHub account
Blog
Twitter profile
Kaggle profile

Make sure your accounts show your full name with a professional-looking photo. Use technology to your advantage - being anonymous on the internet makes it really challenging for employers to find and research you. They'll just move along to the next candidate.

Check out the blog by Rachel Thomas entitled Making Peace with Personal Branding. There is sage advice thereon:

Why personal branding is useful
Twitter for beginners
Speaking at conferences/meetups
Public speaking resources

Kaggle Competitions

Participating in Kaggle competitions has numerous benefits:

there's a friendly community
you can work with others in a team
you become familiar with popular datasets

An added bonus is there are kernels, which is code in Jupyter Notebooks that others have shared. You are free to copy and use them to get started on a competition. The code is available in both R and Python.

You can set up your public profile and share your work. It's also a lot of fun.

Blogging

Blogging is a way to display your communication and writing skills. Writing about a project or a data science topic allows you to share with the community as well as encourages you to write out your work process and thoughts. This is particularly handy when interviewing.

Check out David Robinson's article Advice to Aspiring Data Scientists: Start a Blog. The blog includes:

possible blog topics
creating a portfolio
getting feedback

David shares his experience in social media and how he obtained his first data scientist job. He also generously offers to retweet your first data science post. With 20K+ followers, that's an offer that can't be refused.

GitHub: Show Your Code

Sharing your code, particularly in data science where the community relies on open source software, is a great way to show your skills. Whether it's Kaggle competitions or projects, make your code available in a GitHub repository and write a corresponding blog. Employers do look at GitHub accounts of candidates to see their GitHub activity, coding level and projects.

Networking

Networking is critical. A definition of networking is:

interact with other people to exchange information and develop contacts, especially to further one's career.

But it's so much more complicated than that. Emily Robinson has written a couple of insightful blogs on what networking really is, and how to do it both effectively and respectfully:

Building Your Data Science Network: Finding Community

meetups
twitter

Building Your Data Science Network: Reaching Out

offer value
find a connection
follow-up

Classroom

When in a formal educational program, make sure to speak and connect with everyone, not just the folks with whom you feel most comfortable socializing. Make sure your professors/instructors know your name. Get to know every Teaching Assistant in the classroom. Get to know all of your fellow classmates. Every person has different strengths and networks and skills and something to offer, including you.

Twitter

If you're new to twitter, I am sharing some of my favourite twitter accounts to follow. Follow who they follow, follow the insightful (and sometimes fiery) conversations on data science, and build your network. To make it easier, subscribe to this list on Twitter for the one-touch following: Notable Data Scientists

Participate by liking tweets, retweeting or contributing to a conversation.

Education

With 12-week boot camp costing about $16K these days, 2-year university graduate programs costing significantly more, and the costs associated with online data science programs, the investment is significant. Considering the monetary, time and opportunity costs associated with learning data science, do your research. Find out what the placement statistics are and what resources they offer to entry-level analysts/data scientists.

How many / which hiring partners do they work with?
Are there opportunities for internships or small projects?
How extensive is their alumni network?
Reach out to alumni of the program (via LinkedIn) and ask about their experience in the program, as well as job placement support. Get the inside scoop.

Underground Resources

Almost every meetup group or organization has a community Slack team you can join. You can find this information in their announcements, join their Slack teams and watch out for the job postings.

Often, at the start of a meetup, attendees will share any job openings at their company. It's a convenient opportunity to speak to them for more information.

Subscribe to data science newsletters. Here's a list of 7 awesome data science newsletters to keep you informed.

Read your emails. Meetup groups and newsletters regularly post job opportunities that are more "real" than the postings that appear in the well-known job boards such as LinkedIn, Glassdoor, Indeed, etc.

Managing Expectations

If the title is other than "Data Scientist", such as "Analyst" or "Junior Data Scientist", and it allows you the opportunity to use and refine the data science tools you've learned, that is most important. Your first data science job may not be the job of your dreams. You may not have the exact title you envisioned. And it may not be for a tech company that is a household name. Think of it as a learning experience and stepping stone. As long as you are moving in the right direction, that is good progress.

The article was originally published here

Source: HOB

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