Assessing a Company through Its Maturity
The Great Upgrade Assessment Part 2A
This article series is intended for data practitioners of all levels, especially those interested in leadership positions and in advancing their careers. You may be more interested to lead as a technical individual contributor than as a people manager, or vice versa. The lion’s share of this article has been excerpted from Chapter 10 of How to Lead in Data Science.
As discussed in The Great Upgrade introduction article, now is a prime time to take stock of your career and consider potential avenues of advancement to capitalize on The Great Upgrade. In the previous article, we examined the industry considerations to evaluate opportunities. In this article, we will focus on the company considerations. Keep in mind new opportunities may often be within the industry and the company you are currently in.
Tens of thousands of companies worldwide have data scientists on their staff, and more are starting to hire. Picking a good employer can be the difference between a career-launching pedestal and valuable time lost to ad hoc busy work. What would you be looking for in a prospective employer?
You can look at companies based on their maturity stages and their standing within their industry. This perspective is illustrated in Figure 1.
Company maturity stages include early stage, growth stage, and mature stage. The maturity stage largely determines the types of Data Science projects required and the types of leaders required to lead them. In technology industries, companies compete to be the de facto standard for a technology or market category. Many industries eventually mature to having one company in the leadership position with a few contenders and many smaller players. The standing of a company in its industry can determine the resources available and the focus of its Data Science activities.
Company maturity
Early-stage startups iterate on product/market fit. There may not be much internal first-party data to work with. You can use government and private third-party data sources to understand early customer segments and evaluate early indicators in the buyer journey, onboarding, and product engagement. Companies in this stage are often pre-revenue or have limited revenue from early customers. Venture-backed companies will often have a seed round or series A funding.
Growth-stage companies have found the product/market fit for their offerings. For venture-backed companies, this corresponds to companies with series B or later rounds of funding. These companies have onboarded multiple customers and are expanding their customer base. They have often reached $50 million to more than $1 billion in enterprise valuation and are looking to scale operations for existing products, while developing new product lines and expanding to more geographies to serve additional customer segments.
Growth stage is the stage when many companies start hiring Data Science leaders. A company in the growth stage accumulates first-party data to understand the LTV of its customers; estimates the ROI for strategic projects; helps marketing increase customer awareness; optimizes channels for customer acquisition; analyzes the velocity of new feature adoption; and optimizes for activation, revenue, and referral funnels. With the variety of possible value-adding Data Science capabilities, there is significant room for a Data Science team to produce business impacts.
You can identify these high-growth companies in the United States by referencing the Wealthfront career-launching companies. See if there are any in an industry you are passionate about and in a geography of your interest.
Mature-stage companies can be private or public. They often have scalable and repeatable business models with stable customers and predictable recurring revenue streams. In mature companies, Data Science efforts can focus on revenue optimization, retention, and feature adoption. One particularly impactful effort is to operate a robust A/B test infrastructure with high precision to measure incremental improvement in key metrics. In a mature business with broad reach, even marginal improvements of 0.5% in key metrics can significantly impact revenue.
Many mature companies are pioneers in utilizing Data Science capabilities and have built up large teams to serve their business needs. Apple, Amazon, Airbnb, Google, Facebook, Microsoft (which owns LinkedIn), Netflix, and Uber are the well-known mature companies in the internet space. Many may not realize that Capital One, JPMorgan Chase, and Wells Fargo in the financial industry all have over 100 data scientists on their teams. In the healthcare space, Aetna and UnitedHealth Group also have over 100 data scientists on their teams.
In the United States, the top 100 employers of data scientists (as ranked by their Data Science team size) have teams with 50 to more than 1,000 members. These 100 employers together employ 30% of the practicing Data Science workforce per Global Talent Trends 2020 by LinkedIn. If you are looking to manage a large team of data scientists, these companies provide good opportunities.
In the next article, we will examine the second angle: a company’s standing within its industry, and how that plays into examining a next-play opportunity. In the meantime, The Top Places to Work for Data Scientists by Jike Chong, Ben Lorica, and I identified top companies for data scientists at different career stages to work for, and The Top Places to Work for Data Engineers by the same authors identified top companies for data engineers. You can use these as reference points in your assessments of companies. We will reference these again when we discuss the second angle, a company’s standing within its industry and related considerations when evaluating the opportunities for the next stage of your career.
If you have views, questions, or examples you would like to share, feel free to comment below or contact us directly.
About the authors:
Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies.
The writing and opinions expressed are solely our own and are not shared, supported, or endorsed in any manner by our respective employers.
