Here is the second part of “Data Science Career Advices from Experienced Professionals” series, gathering precious advices from experts of the industry on shaping your career in data science/analytics fields (you can see the first part of the article by clicking here).
As the analytics world continues to grow, the exploding demand for analytics talent creates endless career opportunities in data science. We see that for people that are just starting out on their journey, there are many questions to be answered.
At IADSS, we believe that building a career in data science does not have to be so complicated and are working towards defining standards on the various career options in the industry. If you want to be a part of this initiative and help collectively define industry standards, you can take a 5-minute survey. More details are provided at introduction pages of the survey.
Variety of the Tools
Data scientists trust tools to help them gain insights from data and selecting the right tools and techniques is certainly critical to be successful in any data science project. However, according to Robert Chang, in the beginning, you should focus on the problem rather than the tools.
"Instead of fixating on a single technique or programming language, ask yourself, what is the best set of tools or techniques that will help you to solve your problem? Focus on problem-solving, and the tools will come naturally."
-Robert Chang, Data Science at Airbnb
"Knowledge is knowing that a tomato is a fruit, wisdom is not putting it in a fruit salad.”
- Miles Kington
Knowledge is knowing how to perform an ordinary linear regression, wisdom is realizing how rare it applies cleanly in practice."
-William Chen, Data Science Manager at Quora
Curiosity Makes a Data Scientist
Asking questions is not only a vital part of learning but also for performing well as a practitioner of data science.
"Ask questions. Not only when you are learning the tools and trade. Ask questions all the time. Every time you get new data, new tasks, new users and new challenges. The essence of the job is to constantly ask new questions and build new insights. Questions are the seed."
-William Emmanuel Yu, CTO at MDi
"Much of the contribution of a data scientist (and why it's really hard to replace a data scientist with a machine), is that a data scientist will tell you what's important and what's spurious. This persistent skepticism is healthy in all sciences and is especially in a fast-paced environment where it's too easy to let a spurious result be misinterpreted.
You can adopt this mindset yourself by reading news with a critical eye."
Meet with People Who Love Data as Much as You Do!
Meet-ups, conferences and community events are good places for meeting with other data scientists from different industries and backgrounds, and great places to learn and share experiences.
"Plug Yourself Into the Community (Check out Meetup to find some that interest you! Attend an interesting talk, learn about data science live, and meet data scientists and other aspirational data scientists.)"
-William Chen, Data Science Manager at Quora
Data Science Salary and Compensation
For some (or many), part of the attraction in a data science career is the opportunity for a higher salary. According to Satvik Beri, "value creation" is the keyword!
"One of the key variables in earning more is simply being able to add a lot of value to a company. The main weakness of most data scientists is that they're very invested in Math & Technology but don't really understand the business."
-Satvik Beri, Data Scientist
What If You Don’t Like It Afterwards?
For people that get into data science and realize that it’s not for them, what can they transition into? Well, this can happen all the time, in all fields, to anyone. According to Kevin Novak; you can figure things out along the way and evolve.
“If you don’t like mathematics, a better and more obvious role is to get into business development of a product. On the other hand, if you like mathematics but don’t like programming, an analyst position may be more suitable. If you are good at mathematics and engineering, but not good at communication, I would recommend becoming an engineer”
-Kevin Novak, Chief Data Officer at Tala
Never Stop Learning
A piece of advice on attitude - that would probably apply to all things: Be a lifelong learner!
"Never stop learning. It is my north star. The freedom to think, challenge and learn is the greatest benefit of a career in data science. I’ve had amazing opportunities to learn about search engines, recommendation systems, scaling software, forensics, genetics, being a manager, hiring a team, motivating an organization. Every company is different, all successful products evolve, management philosophies change and software continue to eat the world. What I know today will be largely obsolete in a couple of years. If I don’t continue to learn, I won’t continue to succeed."
-Joe Isaacson, VP Engineering at Asimov
"Don't make overtime and crunch time the norm. You need time off to read, rest and enjoy life. Overworked people are less productive over time, their brain switches off constantly, concentration levels drop. This is true for any profession but given the lack of data scientists and the extreme technical work they do, I have a feeling this profession will have the same curve that IT people had in the 90s."
-Ricardo Vladimiro, Data Science Lead at Miniclip
Help Set the Standards for Analytics & Data Science Professionals
IADSS is conducting a global scale research study. This study aims to gain insight into the analytics profession in the industry and help support the development of standards regarding analytics role definitions, required skills and career advancement paths. This will help set some industry standards which in turn could support the healthy growth of the analytics market.
If you want to be a part of this initiative and help collectively define industry standards, you can take a 5-minute survey as an analytics professional. The answers for the survey will be kept anonymous, only aggregated results will be published.