We identify U.S. organizations that will help you develop your career in data science.
By Jike Chong, Ben Lorica, and Yue Cathy Chang.
What would constitute a good place to work for a data scientist? How do you think about it at different stages of your career?
These are important questions to ponder as data science (DS) practitioners witness the field going through a phase of high growth of 37% per year, fueled by early traction at companies where DS projects are making a business impact. 67% of companies in one survey are looking to expand their DS functions and scale their business impact.
As DS continues to be one of the highest-compensated professions, more people are moving into the field. However, demand for talent is still increasing faster than the supply, with ever more demand for data scientists who can lead.
Depending on your career stage, different types of companies can help you evolve a career in DS. Let’s look at how to quantify this assessment and tailor the opportunities to data scientists of various career stages.
As we look to evaluate US organizations on whether they may be a good place to work for a data scientist, three factors are top-of-mind: employer brand, team maturity, and team growth. At various stages of your career, different factors can be more important than others. We aggregated data from LinkedIn, Glassdoor, and Forbes Best Places to Work to help quantitatively substantiate these factors.
This methodology leans towards larger more established companies. The dataset of potential companies was extensive but not comprehensive. Our methodology focuses on precision rather than recall. In other words, for our analysis, we only included companies where we have sufficient data.
Employer brand: estimated based on the Glassdoor company rating, and appearances on the Forbes Best Places to Work list in the past five years (2016-2021).
Team maturity: estimated based on the number of data scientist profiles on LinkedIn.
Team growth: historical growth is estimated based on the one-year growth of data scientist LinkedIn profiles at a company, and forward-looking growth is estimated based on the open positions in data science on LinkedIn.
Top companies to work for
Let’s look at your career stages in three parts: early career, mature data scientist, and mid-senior leadership positions.
Early-career data scientists
If you are just entering the field of data science as a recent graduate or junior data scientist, stepping into a role that requires not only deep technical knowledge but also nuanced execution of best practices and domain expert knowledge can be overwhelming. Good companies for data scientists at this career stage have large teams with mature data science processes. They also have many job openings posted on LinkedIn. Top companies differentiate themselves with high Glassdoor company ratings and, sometimes, mentions in Forbes’ Best Companies to Work For lists.
The graphic below lists the five top companies with ultra-large 400+ data scientist teams and 25 companies with 50-300 member data science functions with good company culture, as indicated by their Glassdoor company ratings.
Mature Data Scientists
If you have built up experience working on data science projects and are looking to amplify your impact through influencing and leading others in a tech lead or manager role, the growth of the data science function becomes important. Good companies for mature data scientists are experiencing high growth, as indicated by their data science team growth in the past year and new job openings in data science. The data science function is still less than 50 people, and a tech lead or manager can still take on significant responsibilities and grow their impact with the company.
In the graphic below, the top five companies have either grown by more than 30% in the past year or have 10+ job openings in data science. We also list 20 more companies where we see evidence of strong team growth and presence of good company culture, as indicated by their Glassdoor company rating.
Mid-to-senior Data Science Leaders
If you have built up leadership skills and are looking to direct a data science function or to inspire an industry as a mid-to-senior level executive, there are opportunities in large organizations to take on significant responsibilities and lead the data science function. Good companies for mid-to-senior data science leaders are seeing strong team growth in their data science function, as indicated by growth in the past year, as well as new job openings in data science. In data science functions of 50 people or larger, so there are plenty of opportunities to lead large teams in a product line and take on significant responsibilities.
In the graphic below, the top five companies have either grown by more than 50% in the past year or have 100+ job openings in data science. We also list 25 more companies where we see evidence of solid team growth in their LinkedIn data and good company culture, as indicated by their Glassdoor company rating.
There you have it, good US organizations to work for as a data science practitioner. This analysis focuses on larger, more established companies. There are also many earlier-stage companies that can be suitable for mature data scientists to take a leadership role and for senior leaders to get in early to build a powerful data science organization. When specific opportunities come up, you can use the three fundamental factors: employer brand, team maturity, and team size, to evaluate if they are right for you.
The book “How To Lead in Data Science” talks about assessing the industry and the role in addition to assessing the company and the team. To learn how to advance your data science career to take on more responsibilities and amplify your impact, purchase a copy of the book. Use the 40% discount code podexchange20:
- “2021 Business at the Speed of AI Report”
- “Changes to the data science role and to data science tools”
- “One Simple Chart: Data Engineering jobs in the U.S.”
- “2021 Trends Report: Data, Machine Learning, and AI”
Subscribe to the Gradient Flow Newsletter: