Typical Data Scientist Job Description

Written By Alla Levin
February 22, 2021
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Typical Data Scientist Job Description

The Data Scientist job has become a new trend globally and has been declared as the hottest job across the world, as data-driven decision-making is increasing in popularity. It is a revolutionary technology that everyone seems to talk about. Every big and small company is seeking employees who can understand and analyze data. Data scientists have knowledge and skill sets in different areas like Analytics, Mathematics, Statistics, Modeling, and business acumen. These skills help them to find patterns that the organization can use to grab new market opportunities. So this job role is creating hype, by its demand all around the world. 

Nowadays, several candidates and professionals want to become a data scientist as it is a highly paid career. This job role offers an average income of $116,100 per year which makes it a highly lucrative career option. An attractive key factor about data science is that it is versatile.

Numerous applications are available within it and are widely used in banking, health-care, e-commerce industries, and consultancy services. Data scientists can make data better by processing and analyzing it properly. They improve its quality to make it better for their company and allow them to make better business decisions. Today, you can even find data science programs online to help professionals learn everything from scratch. 

This article will tell you more about data scientists and the responsibilities they handle in different organizations.

Who is a Data Scientist?

Data scientists are professionals who have expertise in analytical data and technical skills to solve complex problems. They have the curiosity to identify the problems that need to be solved. They utilize their skills in both technology and social science to find trends, patterns, and insights to manage data. They can also use their industry knowledge, skepticism of existing assumptions, and contextual understanding to uncover solutions for business challenges. 

Their main job is to make sense of messy, unstructured data from sources like social media feeds, smart devices, and emails that don’t neatly fit into a database. They crack complicated data problems with their strong expertise in certain scientific/mathematical disciplines. They draw a lot of information from the scientific field and applications (Mathematics and Statistics). They can make a lot of use of the latest technologies in finding insights and reaching conclusions that are beneficial for an organization’s development and growth.

The main purpose of a data scientist is to extract and interpret data effectively and present it in a non-technical and simple language for the end-users. Thus, a data scientist is a professional who draws useful insights/information, thereby converting it into data-driven products. They pursue the following data science process in five steps:

  • Understand the problem
  • Data Gathering
  • Data Cleansing and Preparation
  • Data Analysis or Model Deployment
  • Data Visualization and Communication

So all these steps are taken up by data scientists while solving problems and finding meaningful insights for their organization.

Data Scientist Job Description

Skills Required for Data Scientist

The data scientist has become a vital job role in any organization, so here are some skills required to become a data scientist:

Skills Required for Data Scientist

There are some educational, technical, and non-technical skills required to become a successful data scientists, such as:

  • Education Data scientists are highly educated as almost many of them have a master’s degree and PhDs, so a very strong educational background is required to gain deep knowledge of this field. A minimum bachelor’s degree in computer science, Statistics, Social science, or Physical Science is required.
  • R ProgrammingThis programming language is generally preferred for the in-depth knowledge of analytical tools. R programming is specially designed for data science needs that can solve any problem encountered in its processes.

Technical Skills- Computer ScienceTechnical Skills- Computer Science

  • Python Coding It is the most common language required in data science roles along with Perl, Java, C/C++ for coding. It has a rich set of libraries that are specifically designed to carry out tasks related to data science.
  • Hadoop PlatformIn several cases, Hadoop is highly preferred with Hive and PIG selling points. Apache Hadoop is ranked second most important skill for a data scientist.
  • SQL Database/CodingSQL is also a must-know skill for the data scientist as it helps to carry out operations such as add, delete and extract data from a database.

Besides the above-mentioned soft skills Apache Spark, Machine Learning and Artificial intelligence, Data visualization, and working with unstructured data are also required as technical skills to become a data scientist.

Data Scientist Job Description: Non-Technical Skills

The Data Scientist job role requires non-technical skills too, such as Intellectual Curiosity, Business acumen, Strong Communication Skills, and Teamwork. These Soft skills are required to solve problems and can make a data scientist’s career polished, bright, and strong.

Job Responsibilities of a Data ScientistJob Responsibilities of a Data Scientist

“A Data scientist is someone who makes the value of data.” There are a few major job responsibilities of a Data scientist, mentioned below:

  • Find and ask the appropriate questions to start the discovery process.
  • Acquire data from disparate sources.
  • Collect, process, and clean the data.
  • Integration and storage of data.
  • To select features, optimize and create classifiers through machine learning tools/techniques.
  • Data mining using state-of-the-art processes.
  • Data investigation and exploratory data analysis.
  • To extend the organization’s data with third-party sources of information when required.
  • Enhance data collection processes to find information that is related to building analytic systems.
  • Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence
  • Measure and improve results.
  • Present results in a clear and useful manner by doing ad-hoc analysis.
  • Building automated anomaly detection systems and constant tracking of performances.
  • Make adjustments based on feedback.
  • Repeat the process with new problems with data.

In today’s data era, becoming a data scientist is a highly desirable career path. Several startups, Fortune 500s, government agencies, and many organizations are finding the value in capitalization on Big Data. So it is the right time to choose a career in data science and find the niche.

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