The Hard and Soft Skills of a Data Scientist

The Hard and Soft Skills of a Data Scientist

Data Scientists continue to be the toughest to find professionals because of the diverse skill sets needed to be a top performer in data science. In the simplest of terms, a Data Scientist takes what the data analysts and data engineers generate and makes business decisions based on both data analytics (hard skills) and business metrics (soft skills).

We crunched the data on recent job postings to see what companies require and prefer when hiring Data Scientists.

Hard skills are much easier to define and recruit for since they are common skills that are typically required by most companies and the keywords are searchable.

Hard skills

  • ___ number of years in Data Science and Data Mining.
  • MA or PhD in Computer Science, Computer Engineering, Statistics, Math or Analytics.
  • Big Data technologies including Hadoop, MapReduce, Hive, Pig, Cassandra.
  • R, Python, PHP, Ruby, Matlab, JAVA, C++, SQl, SAS, SPSS.
  • Multivariate statistics - regression, principal components analysis and clustering.
  • Data-driven predictive model development.
  • Large Dataset experience using Teradata, Oracle or SQL.
  • Business Intelligence tools including Business Objects, MicroStrategy and Tableau.

The Soft Skills are the most dynamic and tough to define when it comes to Data Scientists since these are on the business metrics side. These skills are not easily recognized when reviewing resumes and profiles so it takes a talented professional to highlight these skills in their resume and most importantly, show examples and ROI results. It's then up to the Recruiters and Talent Acquisitions Specialists to recognize these skills and further research throughout the sourcing process.

Here are a few soft skills that are becoming more common in Data Scientist job requirements.

Soft skills

  • Inquisitive, curious and creative.
  • Persuasive communication skills.
  • Communicate to a diverse audience at multiple levels of the organization.
  • Challenge existing best practices, explore new alternatives and introduce new initiatives.
  • Share knowledge and clearly articulate insights to technical staff, management and decision makers.
  • Manage teams and projects across multiple departments on and offshore.
  • Consult with clients and assist in business development.
  • Take abstract business issues and derive an analytical solution.

If you are a Data Scientist and want to be recognized as being at the top of the data science class, don't forget to highlight and build your soft skills.

Article written by icrunchdata
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