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HOW TO CHOOSE THE RIGHT DATA SCIENCE POSITION IN 2022
HOW TO CHOOSE THE RIGHT DATA SCIENCE POSITION IN 2022

July 6, 2022

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With the development of science and technology, the scale of data available to human society is growing fast. From day to day, large amounts of data are generated and stored at all times. When faced with such large amount of data information and how to mine the data representation, data science becomes all the more necessary. Data science is derived from this demand. By learning and using the phenomena that is hidden behind this surging data, many of our routine tasks can be easily addressed.

Mathematics and statistics are the primary assets of any data science professional. In order to benefit from predictive responses from the models, it is imperative to understand the styles of data model. If you’re planning to make a move into the field of data science, or switching into analytics and algorithms, but confused – here are the three main data science competencies, honing which you could find your dream data science role for sure:

  • Mathematics/ Statistics
  • Computer science
  • Business domain knowledge

These are the prerequisite competencies that are required for a successful data science career, though you still need to define the roles and responsibilities for a variety of positions. In order to make your job search and preparation more manageable, you need to figure out the role that suits your skills the best. In general, we can categorize data science positions into two types:

  • Analytics-driven positions: Data analyst, product analyst, data scientist-analysis, product data scientist
  • Algorithm-driven positions: Data scientist-algorithm, data scientist-machine learning, applied scientists, research scientists

Analytics-Driven Roles:

The type of roles that are driven by analytics involve a variety of things such as user data, logs, and system performance data, etc. A certified data scientist will drive business decisions by making recommendations based on a detailed data investigation. It is a technical role that involves lot of communication. It also includes helping businesses in making informed decisions, performing exploratory analyses, defining business metrics and making data visualization comprehensible.

Sample job post requirement:

  • Data Scientist @ Apple (Posted in March 2022)

The job role expects the candidate to be motivated and design data solutions to solve business challenges. They must apply statistical thinking and machine learning methods in an intelligible way and be a part of a larger team. Alongside, 8 years and more experience of data science and machine learning, hands-on programming experience and proved competency in Python or R coding languages are some of their key qualification criteria. A strong knowledge of statistical and deep learning methods, coupled with an exemplary understanding of sales including consumer and enterprise markets is a must.

This is a typical job post of Analytics-driven positions, that clearly emphasize proficiency in scripting and programming languages apart from other functional skills. Technical know-how with soft skills is a must for such a role. (Source – Google, India)

Algorithm-Driven Roles

Under algorithm-driven roles, a data scientist is working on business and product insights along with developing machine learning models for various business purposes such as making business predictions. Stronger coding skills area must as you’d be working closely with software engineers to deploy models and build pipelines for model training.

Sample job post requirement:

  • Director- Data Science- Secondary Research @ Gartner (Posted in March 2022)

A candidate applying for the above position is expected to posses a 12+ years of industry experience with 5+ years of hands-on AI or ML system development experience on real-world industry problems, with special focus on mining, Python, R and NLP. They must closely partner with the technology and data engineering team for creating automated Data science pipeline to productionize the solutions. Working knowledge of statistical modelling methods and hands-on experience in handling unstructured data sources will be an added advantage.

This is a typical job posting of Algorithm-driven role wherein technical expertise and proficiency in ML is the key to run the entire business procedure to success. (Source: Google, India)

Which role is best suited for you?

On one hand, analytics-driven role focuses on getting business insight from data, a technical position coupled with soft skills proficiency. Whereas, on the other hand, algorithm-driven roles are more technically focussed. The former tends to drive in more demand in the job market as compared to the latter. The bigger question arises as which role you should take the plunge into? This clearly depends on your choice, the skillset you possess and the level of career graph you are at present.

. If you’re a data scientist with a couple of years of experience at hand, the market is wide open for you with any role you wish to dive-in. As a raw graduate with practically no business experience or even for that matter as a career changer, it is advised to try your might at analytics-driven roles to begin your journey into data sciences. Towards the close, it is always a great idea to figure out what position to aim at, that will provide you the much-needed direction as you scale higher.


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