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Job Details

Data science Full time

  • Decatur, Georgia, USA

  • Post Date: Apr-24-2024

The data scientist is a relatively new key player in organizations ? a new breed of analytical data experts. They are part mathematicians, part computer scientists, and they rule the world of big data. Businesses today are wrestling with volumes of unstructured information that?s a virtual gold mine, which can help boost revenue when unearthed. But they really need professionals who can dig in and find valuable business insights, sifting through the useless chaff and finding the precious nuggets of data. That is what the data scientist does; that is why they are highly sought after and well paid.   

Whether it?s the thrill of problem-solving or the hefty paychecks, the perks of being a data scientist are luring many IT professionals to take this career path. If you?re one of them, this article has everything you need to know about how to be a data scientist. 

Today, we?re covering the data scientist job description ? what is a data scientist, what does a data scientist do, the qualifications for a data scientist, the data scientist roles and responsibilities, data scientist skills, how to be a data scientist, data scientist requirements, and the data scientist career path.

A Data Scientist is a professional who collects large amounts of data using analytical, statistical, and programmable skills. It is their responsibility to use data to develop solutions tailored to meet the organisation"s unique needs.

Organizations are increasingly using more and more data in their everyday operations. A data scientist interprets the raw data and extracts valuable meaning out of it. They then use this information to find patterns and develop solutions that an organization needs to grow and compete. 

Wondering what exactly constitutes the job of a data scientist? Well, no points for guessing, a day of a data scientist revolves around - as the job title suggests - data and data everywhere.  

Responsibilities

  • Data mining or extracting usable data from valuable data sources
  • Using machine learning tools to select features, create and optimize classifiers
  • Carrying out preprocessing of structured and unstructured data
  • Enhancing data collection procedures to include all relevant information for developing analytic systems
  • Processing, cleansing, and validating the integrity of data to be used for analysis
  • Analyzing large amounts of information to find patterns and solutions
  • Developing prediction systems and machine learning algorithms
  • Presenting results in a clear manner
  • Propose solutions and strategies to tackle business challenges
  • Collaborate with Business and IT teams

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