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In an increasingly technological world, data scientist and analyst roles have emerged, with responsibilities ranging from optimizing Yelp ratings to filtering Amazon recommendations and designing Facebook features. But what exactly do data scientists do? The parameters of this role are rarely strictly defined, but data-oriented work has become imperative to the success of all technology companies.
The full job description depends strongly on the type of company. You may find yourself with an unfamiliar set of new tasks when switching from a start-up to a mid-size company, or to FAANG (Facebook, Amazon, Apple, Netflix, Google).
Interested in learning more about FAANG companies? Read these company guides about Facebook, Amazon, Apple, Netflix, and Google!
But what type of company fits best for you? The answer lies in realizing the major differences between these types of companies– the type of work, the expected experience, the prioritized skills– all of which contribute to a more holistic understanding of precisely what the role entails.
Startup companies describe those emerging in a fast-paced business world, rapidly developing an innovative product or service. The U.S. Small Business Administration officially describes a startup as a “business that is typically technology oriented and has high growth potential”; high growth potential referring to employees, revenue, or market. This type of company is unique in mainly two aspects: diversity of work and a low head count.
Diversity of Work
A data science role at a startup company involves a little of everything. It requires a jack of all trades with knowledge in data engineering, machine learning, analytics, data visualization, and work that may not be traditionally characterized as ‘data science’.
You might be expected to dial into marketing meetings, or work closely with engineers to deploy models and build out engineering pipelines. The biggest benefit from working at a startup is the acquisition and development of diverse skills, which is rarely seen in larger companies. As a data scientist at a startup, expect to be tasked with problems where you have to “figure it out”. This results in lots of self-learning, self-pacing, ownership and independence.
Low Head Count
Because startups have fewer employees, it would be much easier to receive a promotion as the company grows. However, a low headcount is a double-edged sword. A smaller company with less people usually has less funding, which on average means a lower salary when compared to larger companies. Thus, a common career path is to start at a larger company, gain experience and receive a higher salary, then transition to a startup for a more diverse experience and career advancement.
Although the career ladder at a startup may be easier to climb, you won't have as much work-life balance. The faster pace of a startup results in a constantly-changing and dynamic environment– and while becoming a director could be possible within a few years, the skills necessary to build a successful business require much more time and perseverance to hone.
FAANG is an acronym that represents the top five performing technology companies: Facebook, Amazon, Apple, Netflix, and Google. These tech giants differ from startups in four main areas: efficiency, processes, responsibilities, and career trajectory.
Note: We refer to data scientists at FAANG companies exclusively in this section, however the described role also represents the data science position at other large tech companies with a high employee count.
Global technology superpowers have tens of thousands of employees, all of whom perform their own unique tasks. Work output is measured precisely, and members of teams are placed in a hierarchy. In this sense, work life is imbued with order– tasks are well-defined, employees report to one boss, and employee success is measured. Compared to the more fluid nature of a startup position, this role is more straightforward to manage and understand.
In an experienced and well-managed company, a transition from academia or previous employment to this role will be seamless. Bootcamps are a common resource that prepare future employees with the necessary skills for their role across several divisions.
The average work experience will revolve around analytics and creating dashboards. Whether it is analyzing cohesive company performance or the success of a certain feature, the data analytics job will be pretty straightforward.
As mentioned earlier, it is generally harder to climb the career ladder at a FAANG company. However, it may be easier to make money as an individual contractor (IC); the role generally entails a deep dive into both optimizing and producing products. The career ladder is wildly different than one at a start-up, climbing to a director position can take decades of commitment.
For example, a typical career ladder at Amazon may go from Business Analyst to Business Intelligence Engineer to Data Scientist to Research Scientist, with each subsequent role having more pay. Each role also has four ‘stages’: levels I, II, III (Senior), IV (Principal). As seen in the tiered hierarchies within these companies, there is a clear-cut path to promotion– but also many more stages to ‘complete’ compared to a similar promotion at a startup.
Although exact definitions vary across industry and countries, according to the Organization for Economic Cooperation and Development, a mid-size business generally has between 50 and 250 employees. This type of company can be seen as the middle ground between a start-up and a FAANG company.
As the rapid growth phase of a startup plateaus out and the company begins to feel the pressure of the market and competitors, mid-size companies experience what is fittingly described as, “growing pains.” On the employees' side, a sense of balance is achieved between the freedom of startups and the structure of FAANG. In this sense, while the data scientist role is designed to adapt to different needs, there is simultaneously a clear set of responsibilities to fulfill.
Finally, while the negatives are balanced on an even ground, the benefits are split as well. The average salary for a data scientist at a mid-size company will be more than at a startup, but less than at a FAANG company. The opportunities for promotion are also inbetween that of a startup and a FAANG. Although being a major contributor to the company is not guaranteed; with patience and perserverance, it’s possible to scale a team and bring great value to the company.
You may be asking, "What size company is the best for me?"
A person's ideal company size largely depends on that individual’s personal goals and priorities– is it payment, promotions, or diverse experiences? Or perhaps a mixture of all? Nonetheless, given that the data science revolution across the globe is contintually growing, one question reamins: "How do I find a data science job?"
The answer is: Check out Interview Query!