Thriving as a Data Scientist in the New Roaring Twenties

This article first appeared here on January 6, 2020

Image credit: designbolts.com

Ever since the job title was coined in 2008 by D.J. Patil and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook, 145,000+ professionals around the world have entered the field and proudly present themselves as data scientists.

It’s 2020. How is this field fairing in the new decade?

The New Gold Rush

$95,000 is the median annual base salary of an entry level data scientist.

For the first time, data scientist has surpassed software engineer (at $90,000 median annual base salary) in 2019 to become the highest paying entry-level job in the United States.

If you are a data science practitioner who has recently entered the field, congratulations!

When a field is offering a 6-figure salary with 2 years of experience out of college, what happens next?

100+ schools are now offering online and on-campus Master’s Degree in data science, and more schools are offering data science undergraduate degrees.

10,000+ data science graduates will be joining the workforce each year, and more talents are attracted to the field from other disciplines through intensive fellowship programs and online MOOC courses!

With rapid growth often comes flocking, followed by job commoditization and reduction in ability to command pay. As many new entrants enters the field, entry-level data scientist salaries are already plateauing, according to a recent PayScale survey.

Similar commoditization trends have been observed in other job functions. In the legal space, after decades of strong growth, starting in 2010, there was an over-supply of new law school graduates. For the Class of 2011, the median starting salary for law school graduates fell by nearly 17% compared to Class of 2009.

If we don’t pay attention to what differentiates between ourselves and new data science graduates, we may well get left behind. How can we ride on the popularity of data science? More importantly, how can we stand out and stay competitive?

(Career) Challenges with Nimble Teams

With the large human capital investments required for data science teams, team size at most companies have stayed relatively nimble.

95% of the companies employing data scientists have teams of fewer than ten data scientists.

Many data scientists are embedded into business product lines, or business functions such as Marketing, Operations, and Customer Service.

How does one advance a career in data science when in a small team, or when embedded in a cross-functional team? What does such a career path look like?

For many data scientists, career advancement may mean starting as a data science intern, then data scientist, and then senior data scientist. But then what?

What do vice presidents of data science or chief data scientists do? How does one make a decision of whether to work toward these roles? And if one is interested in heading that way, what would some milestones look like?

Two things are clear:

1. Hard work alone is not going to get one to senior/executive data science roles
2. Only a small fraction of data scientists will get the opportunity to take on senior leadership roles to produce outsized impact and earn significant rewards

Many other things, are not as clear.

A Different Perspective

As tens of thousands of new data scientists join the race to develop impactful careers in data science each year, the field will become increasingly competitive and one will need more than tips, tricks, and ordinary career advice to thrive and advance a career in data science.

In this blog series, we explore insights, learning and best practices from leading data science practitioners on how one can present powerful narratives and act with appropriate moods in acquiring the necessary knowledge, skills, and experience over a key career span of five to fifteen years.

We reference sage learning and career advice that withstood the test of time as well as observations and interpretations from modern analytical psychology, all have long explored the topic of human and career maturity.

These blogs are by data science practitioners for data science practitioners. We strive to comprehensively explore topics on career advancement in data science. We will reference the path highlighted in Confucius teaching “The Great Learning” and how it speaks to the process of cultivating oneself, managing a team, governing a function, and inspiring an industry, as four major career milestones. The first topic will focus on the process of cultivating oneself.

Your input on the topics is always welcome. Please share your questions/topics here.

As we embark on a new decade, we look forward to exploring the paths to Data Science career growth with you! 

About the Authors:

Dr. Jike Chong is a director of data science at LinkedIn, leading a team to develop products to advance people’s careers in the context of the hiring marketplace. He previously built and grew several high-performing data science teams at Acorns, Yirendai (NYSE:YRD), and Simply Hired. Dr Chong also advises several startups and teaches at Tsinghua University.

Yue Cathy Chang is an executive leader in business development, sales and marketing for data products and services. Cathy has held strategic roles serving customers using data science- and engineering-based solutions at bluechip software enterprise companies and startups. Well-versed in tech and business, she has frequently spoken at data and AI events.

The writing and opinions expressed are solely our own and are not shared, supported, or endorsed in any manner by our employers.