Data Scientist Job Description

This Data Scientist job description template can be posted to online job forums and career pages for the recruitment of candidates. The Data Scientist job description, its requirements, and responsibilities given in this template can be modified according to the specific nature of your company. The primary job role of a Data Scientist is to:

  • Collect, process, analyze and store data
  • Convert the raw data into valuable business information and present it using visualization techniques
  • Build models to address problems as soon as they arise

Data Scientist Job Description

We are hiring a Data Scientist for analyzing bulk of raw data to find the information trends and patterns that will benefit our company. Your goal would be to analyze the market trends and build relevant information models based on them so as to help us make better strategies for our company.

The ideal candidate for this job should possess problem-solving and critical thinking skills as well as a strong passion for research and machine-learning. You must also possess advanced knowledge of mathematical and statistical analysis. So, if you like to work in a challenging environment, and qualifies the following requirements, we would like to hear from you.


  • Bachelor’s or Master’s degree in Data Science, Computer Science, Software Engineering, or other relevant fields
  • Past experience as a Data Analyst or a Data Scientist
  • Expert-level knowledge in data mining
  • Experience with data frameworks like Hadoop as well as business intelligence tools like Tableau
  • Knowledge of Scala, Java, C++, R, SQL, and Python
  • Sound understanding of machine-learning as well as research operations
  • An analytical mind with a problem-solving attitude
  • Ability to take quick decisions and work independently
  • Advanced knowledge of mathematics, especially statistics
  • Excellent interpersonal and presentation qualities


  • Identifying valuable data sources and automating data collection processes
  • Efficient and accurate processing of structured as well as unstructured data
  • Analyzing bulk information for the purpose of discovering market trends/patterns
  • Developing machine learning algorithms and predictive models
  • Combining models by ensemble data modelling
  • Presenting valuable business information with the help of visualization techniques
  • Proposing strategies and solutions to overcome business challenges
  • Collaborating with product development and engineering teams