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Startup vs MNC: Your Best Bet for a Career in Data Science!

Have you just finished your data science course? Now, do you want to test your skills in the real world? The career opportunities are endless in this sector, especially in today’s world where information and data are in the driver’s seat. 

While multinationals have won this race, startups are not far behind. Deloitte had 45,112 data science job openings in January 2022. PwC, EY, and Amazon had 30,000-plus job openings. These numbers show the recurring demand for skilled data scientists. 

The opportunities in terms of career options are extensive and involve multiple streams. Nevertheless, here are some common roles and responsibilities that as a data scientist you need to fulfill:

  • Identifying valuable data sources
  • Automating the collection processes 
  • Processing of structured and unstructured data 
  • Analyzing large amounts of data for discovering the trends and patterns in it 
  • Building predictive models
  • Constructing machine learning algorithms for data handling 
  • Combining various statistical models through ensemble modeling 
  • Presenting information using various data visualization tools and techniques 
  • Proposing solutions and strategies for overcoming business challenges 
  • Collaborating with engineering and product development teams for automating the whole process and developing a neural network out of it

Based on these responsibilities, a data science fresher can go into distinct roles. You could become a data analyst, data scientist, business and marketing analyst, machine learning engineer, system administrator and architect or data engineer to name a few.

But the question still prevails, should you opt for a job in an MNC or a startup? The answer is, it depends on your preference. Let’s look at both the advantages and disadvantages of working in an MNC and a startup. 

Advantages of Working in a Startup 

First of all, let us answer the question, why and when a data scientist should opt for a job in a startup? This will be a simple way to assess the pros and cons of working in this type of company.

  • Different job roles – Typically, in a startup, everyone has to work hands-on to solve various problems. This gives you a chance to work on various things, so you end up performing different job roles. Since the amount of learning is great,  you will become fit for more roles in the future. You could be eligible for the role of a business analyst and data scientist. 

After quitting this job, you could take up any role of your choice when you go to another company. Working in startups allows great flexibility.

  • Builds up an impressive resume – After performing varied responsibilities in a job, you will have a great portfolio. This is the richest reward you can take back professionally. Imagine the job prospects after slugging it out two or three years in a startup.
  • You are abreast of the newest trends – If you are in a data science job, then you need to be up-to-date all the time. Who knows what could turn out beneficial for your job?

These are some of the pros of working in a startup. Now, we head over to the cons of working as a data scientist in a startup. 

Disadvantages of Working in a Startup as a Data Scientist

In a startup, you might get to learn all the processes involved in the job. But, there are certain limitations as well.

  • Workload – One of the highlighting disadvantages of working in a startup is the work pressure. Since you are likely to be a part of a small team, you may end up working extra hours.
  • Performing low-end tasks  – You could be stuck in tasks that are more day-to-day and less of strategic decision making. 
  • Being misled – This is not true for every startup. But, it happens a lot. There have been some startups that have irrelevant knowledge base for their fresher data scientists. Due to the lack of training by experienced professionals, you could end up in a no man’s land and require additional training. 

Nevertheless, working in a startup can be a life changing opportunity in various terms. Since you will be a part of a team that will build the business, the exposure is enormous. 

You will get a chance to work on different projects and get to explore different avenues of data science as a professional. Additionally, if you can make this work, the weightage it brings to your CV is enormous.

Advantages of Working as a Data Scientist for a Multinational

  • You get a good compensation – These are big corporations with tons of money. Your salary will come on time. You might also get employee stock ownership plan (ESOP) options as a part of your salary. After gaining enough experience and climbing up the hierarchy, you could be earning a handsome salary.
  • You have a clear cut job role – Unlike a startup, you will not have a melee of job responsibilities. There will also be no myriad tasks which go on and on in an infinite loop. You will have a healthy work-life balance as a result.
  • Proper guidance – If you are working in a multinational, you can assure yourself of being guided along the right path. There is minimal politics behind the scene, thanks to job security. So, your peers and seniors will give you a correct roadmap for elevating your career.

Disadvantages of Working in an MNC as a Data Scientist

There are certain disadvantages of working as a data scientist in an MNC. Do not be afraid as they are limited in number. The impact is also not high. But, they are unwanted all the same.

  • Superficial learning curve – You learn the basics since your job is clear cut. The protocol is given and you have to just follow it. So, you cannot manipulate data as you would have done in a startup. The scope of experimentation with data is too less.
  • Your resume is just like any other data scientist’s resume – The portfolio remains the same as any other data scientist. You need an edge over others. But, because of the limited job opportunities, you miss out on that extra leverage.
  • Your career becomes stagnant after a while – If you have a secure job, there are chances you might lose that hunger to excel. So, you try less. As a result, you might not be abreast of the newest market trends. Also, there is a chance your career might become stagnant after a while.

Overall, startup or MNC is not a fair question to ask. It has a subjective answer because it depends upon your expectations, priorities, and preferences. 

As a fresher, we suggest you get as much experience as possible. This is possible only when you work in a startup. But, after you gain experience, you need to have steady growth in your career. This is where working at an MNC might make more sense.

A comparative analysis of working in a startup versus an MNC presents a mixed bag of results. Clearly, you get to learn more in a startup. This means you will have an enriched experience in this organization. But, the workload could be a nightmare at times. 

However, everything is not that bleak because you get to learn more on the job. This aspect should never be ignored because it could result in you building a strong foundation.

But, an MNC could provide you with new explorations which might not be covered by startups. You also get opportunities of working overseas with newer clients. But, if you join an MNC as a fresher, the responsibilities could overwhelm you in the beginning. 

Without getting relevant job training, it is extremely difficult to climb the data science corporate ladder. A senior role in data science requires a lot of training around the same responsibilities, we have mentioned earlier in this job.

So, what is the clear cut answer to this question? We would suggest that you should try to get the best of both worlds. A proper roadmap would be that you should get into a startup first. After spending a year or two in the startup, you should try to get a bird’s eye view of the entire career scope in data science. 

After that, you should gear yourself for a career shift. Slowly and steadily, work your way through and analyze hard. What do you want in your career? If you feel that an MNC is better than a startup in terms of work-life balance or any other aspect, then you should make your move accordingly. Otherwise, there is always a startup in existence which requires your services.  

But to have a good career, you need to enrol in the best data science courses. A professional data science certification course can also help you move ahead in your career. If you do not have the time for a full-time course, choose a good part-time data science online course. The better your data science course is, the better are your job prospects once you graduate from it.

For getting a start as a data scientist, you can enroll yourself in a data science course for beginners from Hero Vired. Notably, Hero Vired is offering one of the best data science courses in India. This course will give you a detailed insight into the data science career. Find out what part of the course grabs your interest. 

Work towards mastering the data science skill. After that, think and decide whether you want to go for a full-time course or you want to apply for a job straight away.  

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