Data Science Engineering Careers (Data Scientist jobs)

Best Data Scientist Jobs of the market

Find your next job in data science engineering. Check out the best job offers for data engineers from top companies.

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About Data Scientist Jobs

Data science has become a huge and integral part of the business sector and industries, thanks to the massive amounts of raw facts that various companies and organizations produce and analyze daily.

With this growth, most companies and organizations are implementing analysis techniques to improve their business and increase customer satisfaction. Because of this, the professionals are on high demand. Read on to learn more about data scientist jobs.

What is a data scientist?

These are analytical experts or a professional responsible for collecting, analyzing, and interpreting complex raw facts that help in decision-making in an organization. These professionals are skilled in exploring some of the technical problems that need solutions in an organization.

Data scientist jobs bring together several aspects of technical and traditional jobs, including computer programming, mathematician, and scientist. Some common advanced analytical techniques used are predictive modeling and AI combined with various scientific principles.

These professionals analyze various raw facts in a business or organization to develop meaningful insights. These experts come up with straightforward solutions to problems in a business by following several procedures. Below are some tasks they do.

  • Finding problems in an organization by asking questions to gain understanding
  • Deciding on the correct sets of variables and fact sets to use
  • Bringing together structured and unstructured facts and figures from different sources in a business organization or company
  • Developing algorithms and various fact models that help to forecast expected outcomes
  • Processing raw facts and figures and converting them to an easy-to-analyze format. Such figures helps clean and validate the collected facts to bring uniformity, accuracy, and completeness.
  • Feeding the rendered facts into the analytic system while analyzing and capturing the trends and patterns
  • Working with several programming languages
  • Interpreting the already rendered facts to find opportunities and come up with the needed solutions
  • Preparing results and insights on a particular analysis and sharing them with the appropriate stakeholders in an organization

The skills required are into two categories: technical and non-technical skills, and below are some of them.

Technical skills

Programming

You need to have the skill and knowledge of working with different programming languages like python, R programming, java, SQL, and C/C++, among many others. This knowledge and skill enable you to manipulate raw facts and figures and develop appropriate algorithms depending on the task you are performing as a scientist.

Machine learning and AI

A proper understanding of machine learning will enhance the value you bring to the field and even help you work better and faster. With these skills, you will understand and determine when it is appropriate to use AI. You can also train and assign models to develop effective AI solutions.

Data preparation

This is the process of preparing raw facts and figures for analysis. This process entails discovering, transforming, and cleaning the various tasks and is crucial to analytics. This skill allows you to source, gather, arrange, process, and model raw facts.

Maths and statistics

Just like programming, maths and statistics are critical in this field. Experts in this field deal with statistical and mathematical models frequently. You need to apply these skills daily and expand on them to perform exploratory analysis and develop critical patterns and relationships. 

These experts apply statistical thinking to get facts and figures and even critically thinks about the value of various figures to come up with solutions.

Non-technical skills

Business acumen

Even though this field is IT-related, one must have knowledge and skill in the business field. Knowledge in business helps one to use facts and figures to come up with solutions to problems and figure out how the raw facts can bring about growth in a business.

Communication skills

Proper communication is a useful skill in all professional fields. A data scientist is expected to communicate effectively, pass information from the analysis of facts and figures, and get things done. With these easy apply skills, an expert can explain the research and give assumptions and conclusions about the raw facts and figures analyzed.

This field is lucrative, but you should have some certifications to stand out from the rest. A certification validates one’s skills and know-how in this amazing field. In addition to validation, certifications allow you to develop your skills further and get ahead of others. Some common certifications include:

  • Certified Analytics Professional (CAP)
  • Data Science Council of America (DASCA) Senior DataScientist (SDS)
  • Data Science Council of America (DASCA) Principal DataScientist(PDS)
  • Microsoft Certified: Azure AI Fundamentals
  • Microsoft Certified: Azure DataScientist Associate
  • IBM DataScience Professional Certificate
  1. Take up an undergraduate degree in the field or any computer-related field.
  2. Familiarize and learn to specialize in the required skill set, like machine learning, or any other relevant skill set in the field.
  3. Work on various projects in the field to sharpen and develop practical skills.
  4. Get relevant certifications as they show one is well-learned and an expert in the field.

The salary for data scientist engineering jobs varies depending on several factors, including the skill set you have gained, certifications, and experience.

The average salary for a data analyst stands at $126,000, with the highest getting paid $194,430 and the lowest getting $72,210. You have greater value when equipped with a specialized skill like machine or Artificial intelligence learning.