Data Science, ML and Analytics Engineering

Skills for different Data scientists levels

Data Science is a wide range of skills that include varying levels of knowledge and experience. The competencies required for a beginner Data Scientist will be different from those required for an experienced Data Scientist. The note is based on my observations and experience as a Head of Machine Learning and Data Science, led a team of 35+ people and 7 streams: Fintech, Devices, MobileAd and GEO, Computer Vision, NLP, Internal Projects, CVM.

In this note, we will consider general skills, without delving into the specifics of NLP and Computer Vision specializations.

Junior Data Scientist

Let’s make a small remark that we are considering mainly former trainees and graduates of universities / courses for this position. Junior Data Scientist should have the following skills:

  • Fundamentals of Statistics and Mathematics: Linear Algebra, Probability and Statistics
  • Python Programming Fundamentals
  • Basic knowledge of SQL and databases: what databases are, how they are designed in theory
  • Ability to work with Excel and other data processing tools
  • Understanding of machine learning and algorithms such as linear regression, logistic regression and decision trees

Middle Data Scientist

A Middle Data Scientist should have all Junior DS skills plus the following skills:

  • Advanced knowledge of mathematics and statistics such as optimization theory and time series
  • Ability to work with Big Data and use Big Data tools such as Hadoop and Spark. This item is optional and depends on the company.
  • Experience in building machine learning models, the presence of completed projects
  • Data visualization skills and the ability to present the results of data analysis

Senior Data Scientist

Senior Data Scientist should have all Middle DS skills plus the following skills:

  • Deep understanding of machine learning and algorithms
  • Ability to create scalable and high-performance data processing systems
  • Project and team management skills
  • Depending on the company and the type of project, the required competencies may differ. But in general, these competencies can form the basis for the development of a Data Scientist at any level.

Soft skills for Data Scientists

A good Data Scientist not only has technical skills, but also has advanced soft skills. Here are the main skills that are important at different levels:

Junior Data Scientist

  • Ability to work in a team and collaborate with colleagues
  • Possessing organizational skills to plan your tasks and manage your working time
  • Ability to communicate complex technical information in simple language for non-specialists

Middle Data Scientist

  • Experience in mentoring junior specialists
  • Ability to find non-standard solutions to problems

Senior Data Scientist

  • Ability to make strategic decisions and influence business results
  • Experience in training and mentoring younger colleagues
  • Leadership and the ability to inspire others to achieve common goals
  • Good soft skills can help a Data Scientist achieve great results at work and move up the career ladder. They can also improve the efficiency of the team as a whole.


In the note, we reviewed the core competencies for different levels of Data Science specialists. This list of competencies can be used in your company for hiring and grading employees.

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