Note: This research was done based on Data Scientist job postings listed in India.

Learning Data Science can be hard, and finding a job in the Data Science field can be equally hard if you are a beginner. As a beginner, understanding the opportunities and expectations for the companies will help you make better decisions.

I had other questions too.

• What is the minimum related experience I need to have to get an interview call?
• Can I get a holistic view of all the skills needed for a Data Scientist? Which technologies should I concentrate on?
• Is deep learning knowledge necessary to get a job?
• Is domain knowledge required, and are companies asking for it?
• Which domain/vertical should I focus on?

As with any data related projects we start, I started digging through the data. Read and analyzed more than 100 job postings in two job portals across four different cities in India with an experience of 0 to 5 years.

At the end of this post, if you had the same questions as I did, you will be very well informed to make the right decisions. The objective of this post is to help fine-tune your resume with the needed skills and experience levels. If you just started in Data Science, it will help in getting a realistic view of the job market for Data Science for Beginners.

Scope of the research

• This research is limited to the Indian job market. Job markets are specific to each country. To a certain extent, this can be generalized to other countries.
• Jobs posted in the last 30 days were analyzed (23/6/20 to 23/7/20)
• Major cities’ job posting was taken in scope — Chennai, Bangalore, Mumbai, Hyderabad, and some job postings in Tier-2 cities like Gurgaon and Noida.
• A total of 118 job postings were analyzed
• Job postings in naukri.com** and **indeed.com taken for study.
• Keyword used to search for jobs — Junior Data Scientist, Data Scientist
• Experience restricted to 0 to 5 years — i.e., this is the minimum experience requirement used for job search.

Some companies call Data Scientists with multiple names like Machine learning engineer, Data Science Consultant, Decision Scientist, and so on. All those jobs listed under these different titles but related to data science are taken in scope.

Outliers

I had to filter out jobs that fit the above criteria as they were too specific or too vague. I call them outliers. Here are the criteria

• Jobs with an incomplete job description
• Jobs that are niche like Computer Vision
• Jobs that require only IIT/IIM/NIT graduates
• Jobs that require PhD
• Jobs that are part-time, internship or freelancing
• Data engineer positions avoided — too much into dev ops, building pipelines and assisting Data Scientists, etc.
• Jobs that require extensive software engineering experience i.e., jobs that are decidedly less focussed on Machine Learning and are looking for hard-core developers.

Note: I had reviewed each job description and have prepared a dataset for you to consider. You can subscribe to my newsletter and get a copy of the dataset for further analysis and the actual job posted with company links.

Let’s get to the analysis!

Which city has the most data scientist openings for starters?

If you guessed Bangalore, it’s correct!. Bangalore or Bengaluru India’s silicon valley has the most number of jobs with companies like Amazon, Citi, Genpact, Honeywell posting for Data Scientist positions. Bangalore amounts to around 35% of the job positions analyzed.

If you are starting in Data Science with just a couple of years of related experience under your belt, Bangalore is the way to go. Chennai stands second with 25% of the job positions.

Which platform out of the two — Indeed / Naukri is the best for Beginners?

The word best is subjective. According to me, a site that helps in finding jobs quickly and easily without having to look around for important information is best. Naukri and Indeed had almost the same number of job postings — 61 and 57 postings respectively over the past 30 days. With Naukri, the job search was mostly broad — search multiple job titles like Data Scientist, Data Analyst, etc. at once, and you can select various cities in one single go. With Indeed, the best I could find is to choose one city or a single job title. I found it constraining. Definitely, not the best experience with Indeed.

I also felt that Indeed lists the job in an unstructured manner. Naukri had a dedicated job location, min salary, max salary, part-time/full-time information separated from the rest of the job description. In Indeed, it was all over the place, making it tough for finding information quickly.

Beware! I found less than 10% of the job posting that are duplicates — both posted in Naukri and Indeed. It means that when you are applying for jobs, post in both the sites.

I didn’t do a job search on Linked-in, which is another excellent resource for the job hunt. There might be other sites that you may need to explore.

Is a math background mandatory (formal education) for the Data Science positions posted?

Almost all the positions posted requires adequate familiarity in math with strong skills in Statistics, probability, and other quantitative methods. Still, only 11% of the job openings are very specific about exceptional math skills being mandatory. Otherwise, if you are a self-learner in data science or have a decent math skill, you should still get a job.

What keywords are the best to search for Data Scientist positions?

It is a no-brainer. Data Scientist keyword search would cover Data Scientist, Lead Data Scientist, Senior Data Scientist, Machine learning engineer, and should wrap around 80% of the job openings. If a job opening is not tagged correctly by the recruiters, then you might consider other keywords like AI Engineer, Data Science Consultant, Applied Scientist, Decision Scientist, etc.

The full range of keywords is present in the dataset used for this search. See the “Role” column in the dataset.

Which vertical/domain has the most number of Data Scientist positions?

For starters, domain here refers to the industry type of the company. E.g., Banking, Retail, Manufacturing, etc.

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