Kaggle CareerCon 2019: Top 10 Tips for Getting a Data Science Job

Kaggle CareerCon 2019 wrapped up last week and now that I've finished writing recaps for each day, I thought I'd make a final post noting the key points and advice that came up multiple times throughout the event. Sometimes one person's advice conflicts with another so it's hard to know what to think or whose advice to follow. These were general points of consensus across all the sessions over the 3-day event.


1. Learn by doing. Experience is the most important thing so focus on doing projects as soon as possible and try to get a data job using what you've learned from projects as soon as you can. It is fine to jump in and start a project (or try to get a job) even if you don't know everything you need to know: you can learn as you go.

2. Look for mentors to learn from and communities to join. You can learn much faster from other people than you can on your own. This generally makes larger companies a better place for junior people to start, since they will have more senior people to learn from.

3. Communication skills are very important. A lot of time will be spent communicating with stakeholders and non-technical people.

4. SQL skills are important and among the easiest skills to train for tech interviews.

5. It is important to know the fundamentals of statistics. The scientific method, experimental design, testing hypotheses, basic descriptive statistics, probability and related topics are important to roles across data science. Advanced math and ML techniques are more niche.

6. Tailor your resume (and other application materials) to the position you are applying for.

7. A solid grasp of the basics of computer science and algorithms is important (This was advice echoed by data science research/ML engineer types. This would probably not be as important for data analyst roles).

8. It is a good idea to establish an online presence and a personal brand. Use Kaggle, GitHub, a blog and other social media accounts like LinkedIn to showcase projects and network. Content creation of some form is a good way to put yourself out there and do visible projects.

9. Don't filter yourself out of positions by taking stated job skill and experience requirements on job postings too seriously.

10. Data science is a broad field that encompasses a variety of job descriptions allowing for people with wide range of backgrounds and skill sets to find a place. Although superior education, math and programming skills may be required for research positions at top tech companies, much of what needs doing in data science does not require advanced degrees or complicated math. Doing research on positions you are applying to and targeting applications to those that best fit your interests and skill set will generally yield better results than just applying to every data science job you find.