Top 10 List for Data Science Job Seekers

Over the past six months, I've seen an explosion of advice and insight into data science from people at all levels: senior leaders, veteran data scientists, new data scientists and aspiring data scientists. Over this time, I've seen a number of topics arise repeatedly. I do not claim to own any of this advice as my current thinking is certainly an aggregate of the great things I read on this platform everyday. Here are the top 10 pieces of advice I've come across (in no particular order).

1. Be realistic about your job opportunities and adapt those expectations by company. There are not universal standards for education, work experience and coding background but there are norms within each company. Do your research about data scientists at a company when you apply to understand if you should apply at entry level, senior level, staff level or perhaps even management level.
2. Do your research about the positions for which you apply. Understand as best you can the job responsibilities and the career track of those in similar positions at the company. At some companies, data science could be very new. Reach out to the recruiter and/or hiring manager to understand how the company envisions supporting career development for those in the data science area.
3. Take time to really think about what you want in a data science position. Not all positions are the same. Consider whether you want to be in sales, marketing, product, security or another area. Consider whether you want to be more client facing or back end heavy in your work. Consider whether you want to work within a large company or a small startup (each carries its own unique difficulties and advantages).
4. Network early. Then keep networking. Your network forms the basis for the career in ways you cannot yet even imagine. Treat your network like a data science problem. Identify a goal. Understand what you want your network to do for you. Determine what you will contribute to your network. Evaluate the parameters you use to identify whether you want someone in your network or not. This is not unique to data science but it is definitely foundational for your search.
5. Develop a personalized strategy for applications. I won't tell you there is one right way to apply for positions. If you go the volume route, be prepared to complete a huge volume of applications and to face a lot of rejection. If you go the quality route, make sure you spend a lot of time personalizing each application and be prepared to face a lot of rejection. Notice the commonality? Rejection. Your strategy for applications must include how you will deal with rejection. Data science is a hot field with ultra-talented people in it. Plan how to deal with rejection ahead of time.
6. Be a master at data manipulation. Interviews vary a lot. What they have in common is the ability to manipulate data to do analysis. This includes using SQL, R and Python. There are countless ways to prepare and to master this skill and I won't try to outline them all here. Just count on needing to be great at this skill to get in the door of a data science position.
7. You must understand algorithms but you need not memorize them all. I've read some great posts related to this point but the main idea is that you should understand what algorithms do well and what they do not do well. You should understand how they work. You do not need to memorize every detail about them. No one remembers everything but what good data scientists do remember is how to problem solve. Know your toolset.
8. Your soft skills make a big difference. Soft skills are sometimes underrated in a highly technical field like data science. Don't underestimate the critical importance of being able to communicate a complex idea in a way a 12 year old could make sense of it. Don't underestimate the value of being able to know your audience and adjust the level of technical detail you use accordingly. Don't underestimate being able to describe your work in a 30 second elevator speech. Practice, practice, practice.
9. Keep learning. What I love most about data science is how fast it is changing. Everyone is a student because we must be. Even the experts must take the time to keep pace with the rapid developments in the field. If you are job searching, don't forget to spend time reading about the latest developments in the field. Don't forget to practice your skills to keep you sharp for upcoming interviews. Don't forget that what might get you a job is understanding the latest work in an area of data science.
10. Be persistent. This is easier said than done. It is also the most important of these 10 because without it the others don't matter. Why? Imagine that you open your email to a 10th rejection in 10 days. You have a choice: keep fighting and believe the right opportunity will arise, or don't. The choice is really that simple. If you refuse to give up and keep the other advice above in mind, you will get there.

This list could be 100 items long and not be exhaustive. This list is also a product of my own experience and does not necessarily reflect everyones' thoughts. However, it is a place from which we can begin discussions about getting jobs in this field. I hope you find it useful and would love to hear your thoughts.

Any additions, changes or comments? Thanks!