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One of the critical decisions data scientists face is whether to work for a FAANG vs. Startup. FAANG companies offer risk-free employment, adequate funds, and chances to develop significant projects with modern technologies.
On the other hand, startup jobs are more stimulating; one gets to handle a lot of tasks and has the opportunity to directly contribute to the success of the new business. This means that it is resolved through structuring discussion around the career objective, work preference, and personal development plans. It can help data scientists think through what factors they need to weigh when choosing between opportunities at FAANGs and startups.
Imagine a technology firm you're familiar with. It's likely you thought of Google, Meta, Amazon, Apple, or another large corporation.
Given the well-known reputations of these companies, it's common to believe that employees there are held to exceptionally high standards of performance. However, this isn't always the case (more on that later), but the "halo effect" can still be advantageous for you. Having the endorsement of a major technology firm on your resume can significantly simplify your job search process.
Considering the current employment system, this should be a factor to keep in mind when selecting a company to join.
When we come to the point where data scientists should work, It is a common belief that FAANG firms only employ the most intelligent and skilled individuals. However, this isn't always the reality. Over time, it's been noted that in any location, there's a typical spread of abilities and talents once a certain number of people are involved. This spread might vary slightly along the X-axis, but it remains a normal distribution.
Many of the most famous companies began with strict hiring criteria, but as they expanded and increased their recruitment efforts, the quality of their employees gradually returned to the average. Surprisingly, this means that smaller startups often have more exceptional teams than large technology companies because they have the resources to carefully select each new employee. Indeed, it's important to assess the skills of potential candidates directly during the interview stage.
Your earnings are influenced by a variety of elements, such as the particular organization, your position within it, your negotiation skills, and more.
The key point to remember: It's not solely about the dollar amount you receive but also the stability and liquidity of your earnings. This is impacted by the makeup of your compensation, which includes the balance between your base salary and any equity.
Initial phase: In the early stages of the startups, companies offer comparatively less in terms of remuneration, especially starting wages, but offer the future benefits of greater equity. However, investing in such a potential at the initial stages of a company’s development is a mere gamble, comparable with that offered by roulette. It becomes possible to make millions of dollars within a few years of business formation, and many people dream of having their own companies and never working again; however, the unfavorable position is that most start-ups do not reach this number, and only a few companies become unicorns.
Major tech firms: On the other hand, pay and gender equity are better and less varied within leading tech companies. In all of these factors, the more well-endowed company will pay starting salaries that are higher and the equity that is usually going to be sold and is less risky. This stability is a better alternative than waiting for years to gain equity in pre-IPO companies for maturity.
Expansion phase: Firms in the expansion phase can be a great middle ground; they offer a higher likelihood of a successful exit and still maintain substantial equity potential. When being a part of the leading growing company, there is a strong possibility of achieving a financially rewarding outcome. The compensation in some of these companies can be quite competitive.
Entrepreneurial ventures inherently carry more risk compared to established corporations. Can the founder handle the responsibility? Will you have the opportunity to secure additional funding? Many of these risks are fundamental; in essence, the earlier in the company's development you join, the more probable it is that the company will cease to exist within the next 6–12 months.
At more mature organizations, some of these risks might be reduced or even eliminated.
But there comes a new risk: the risk of being laid off. Startups typically only employ roles that are crucial to the business due to financial constraints.
FAANG often has a greater chance that you will be hired in a role that is later considered non-essential, putting you at risk of being included in mass layoffs.
From FAANG vs Startup perspective, when it comes to the early stages of a startup, your responsibilities are more varied. For instance, if you are the first person hired for data science, then chances are, you will manage different positions like Data Engineer, Data Analyst, and Data Scientist.
Your duties would be setting up the data infrastructure, making data accessible to the business end, creating KPI, testing, developing visualizations, and so on. This is something that will benefit you, as, you will learn and expand your skills in various sub-domain.
Your responsibilities will also likely span the entire company, meaning you could be working with data from Marketing & Sales one day and Customer Support the next.
On the other hand, in a big firm , your tasks will be more specific and focused. For example, you might dedicate your time to forecasting specific metrics.
If you're accustomed to learning in a more structured manner, like through classroom lectures in school, this approach might seem daunting at first. How are you supposed to figure out how to approach these tasks? Where do you begin?
Tackling a range of complex problems is the most effective way to understand the inner workings of a business and develop both your technical and interpersonal skills.
Moreover, established companies often have formal training programs. In a startup, where you're expected to learn on your own, large technology firms usually offer paid educational and development programs.
We've discussed how working for well-known companies can be advantageous when seeking a new position. However, what about the chances for advancement within the same company?
In a startup, your chances for growth are directly linked to the growth of the company. For example , if you become a part of the early team as a data specialist and both you and the company thrive, it is likely that you will have the opportunity to stand and develop a data team.
A significant advantage of working for larger corporations is the wider array of career paths available. If you're interested in working on a different project, you don't have to leave the company; simply switch teams. If you're considering a move to a different city or country, it's probably also feasible.
Stress comes in various forms, and it's crucial to identify which ones you can manage and which ones are intolerable for you.
In the early stages of companies that are rapidly expanding, the primary stressors include:
Shifting priorities: To stay afloat, startups must be adaptable. If the initial strategy fails, it's time to switch gears. Consequently, planning for the future is often limited to a few weeks.
Quick tempo: Early-stage businesses must operate swiftly; after all, they need to demonstrate sufficient progress to secure additional funding before depleting their resources.
Wide range of responsibilities: As noted earlier, individuals in early-stage companies often juggle numerous tasks, leading to a sense of being stretched too thin. In the analytics field, there's a tendency to strive for perfection, but in a startup environment, that's rarely achievable. If it meets the current needs, it's time to focus on the next task.
In bigger corporations, stress arises from different elements:
Complication: The scale of operations in larger corporations introduces a significant level of complexity. This includes a complex technological infrastructure, established procedures, and internal tools that need to be comprehended and utilized. This can be daunting.
Office politics: Within the framework of large corporations, it's not uncommon to feel that a significant portion of time is spent on discussions about team responsibilities rather than on the actual work.
When is the right time to start working for a large corporation versus a startup?
The answer to this question isn't universal. Still, from my viewpoint, it's beneficial to spend some time at a well-established large technology company during the early stages of your career, if feasible.
By doing so, you will :
Enhance your resume with a reputable background that can aid in securing future positions
Experience the workings of a high-performing data infrastructure and analytics organization on a large scale
Receive a structured approach to onboarding, mentoring, and professional development
This approach will lay a strong groundwork, whether your goal is to remain in the large tech sector or venture into the fast-paced startup environment.
Finally, the choice depends on what a person wants from the career and his or her attitude to certain conditions of work at companies like FAANG and choosing a startup. FAANG companies offer unbeatable support, all of which means that learners will have access to the best resources available; they offer stability and focus more on specialization than the general experience and hence should be embraced.
On the other hand, Startups are more flexible, both in terms of employment speed and work roles, allowing employees to have a direct impact on change-making. It means that the decision on the choice of a particular project should be based on a careful evaluation of the advantages and disadvantages of a particular option and reflect the desire of a data scientist for professional fulfillment and a constructive view of work
