06 Apr 2020

How to land a Data Science professional job at your dream company?

According to Forbes, the job requirements for data science and analytics in 2020 are likely to boom to by 364,000 openings to 2,720,000. According to Glassdoor, one of the leading online portals, the national average salary for a Data Scientist is $113,309 in United States. Having looked at data stated above, there is no question why every IT student/professional aspires to become data scientist in his/her dream company. There is nothing wrong in dreaming but the question is how many of aspirants get successful? What makes a different between successful and unsuccessful aspirant. This is the core issue we will discuss in this article. Also, we will share some useful tips, and information which may play a path showing role for all persons looking to make a career in data science field.


  1. Set the goals in beginning – One who is willing to make a career in data science must know what he/she want, set his/her goal, work really hard to achieve that goal, and never settle for less. If we look at diversities of data science field, there are plenty of streams i.e. deep learning, machine learning and artificial intelligence and a few more. There are variety of job profiles in data science field i.e. data engineer and data scientist which looks like similar but are very much different from each other.

    “Data engineers are the plumbers building a data pipeline, while data scientists are the painters and storytellers, giving meaning to an otherwise static entity.” Urthecast’s David Bianco notes

    There is a substantial commonality between data engineers and data scientists when it comes to required skills and responsibilities to be fulfilled. However, the main difference is the focus. In general, data engineers are focused on developing infrastructural models for data generation. On the other hand, data scientists are focused on advanced mathematics and statistical analysis on that data which is generated by data engineers. Therefore, one must decide in the beginning itself that to which job he/she aspiring for. In contrast, data engineers’ job is to provide necessary support to data scientists and analysts in terms of facilitating required infrastructure, methods and tools that will be used to deliver end-to-end solutions to business problems. Data engineers focuses on developing scalable, reliable performance oriented infrastructure for conveying clear information about business insight outs from raw data sources; implementing analytical projects with a focus on collecting, managing, analyzing, and visualizing data for developing real-world analytical solutions.

  2. Learning Statistics – The core of data science is all about statistics. The success of one’s career in data science largely depends on statistics skills. One has to be good in dealing with statistics related topics i.e. Combinatorics and basic set theory notation, Probability definitions and properties, Common discrete and continuous distributions, Bivariate distributions, Conditional probability, Random variables, expectation, variance if he/she wishes to make an impact in data science skills.
  3. Practice live projects – Data science aspirants must undergo for small live projects on regular basis. Such projects may be available in terms of data science internship offers or some organized events by web portals hosted by leading websites i.e. Edwin Chen (http://blog.echen.me/), Hunch (http://hunch.net/), KDNuggets (http://www.kdnuggets.com/), Data Science Central (http://www.datasciencecentral.com/), Kaggle Competitions (https://www.kaggle.com/competitions), Simply Statistics (http://simplystatistics.org/), FastML (http://fastml.com/) and many more. Also, many engineering colleges and professional bodies of government or non-government organizations like “AnalyticsVihdya” or “Machinehack.com” or “kdunuggets.com” organizes data science events i.e. Hackathon 2020 which can provide cutting edge exposures to students/aspirants looking to sharpen their skills.
  4. Follow leading role models in data science field – One who is aspiring to become data science professional must not only follow related web portals but also the senior professionals of the field who post details of their learning, experiences and new projects details via various blogs or online portals via Linkedin or other social network platforms. One must join some local data science meetups, join data science learning groups, link with people in professional network and industry. Interested aspirants can send a personalized greetings note when trying to connect with expert strangers on LinkedIn or other platforms.
  5. Continues Learning – Learning is the key to success in all fields. Data Science aspirants can get fails in interviews like any other field aspirants. But they must take notes on failures and their reasons should be introspected carefully. One must take note of all the interview questions by remembering, especially those questions which they failed to answer. We must remember that one can fail again, but he/she don’t fail at the same spot. We should always be learning and improving for the better future outcomes.
  6. Soft Skills – The skills which are required for any professional job are the way to deal with people, talk to people, sense of dressing up and emotional intelligence. In the era where cross cultural team structures are the key of business systems, the soft skills are equally important as hard skills. All fortune 500 companies prefer to candidates who are a good team player and have better interpersonal skills.


Above mentioned are just an abstract for tips which are based on personal experiences and reading few personal blogs of data science professionals at leading micro blogging websites. Learning new skills, developing professional network and keeping an eye on new developments of the concerned industry are the keys for professional success. We must remember that future belongs to those who believe in themselves.

Author –
Dr. Sandeep Kautish
Dean Academics
LBEF Campus