SOCIETY | May 11, 2017

Data Scientist Professional? The most sought after in 2017

Big data, data analysis, data mining and machine learning are increasingly relevant for the IT industry

According to www.Glassdoor.com, a well-known American website whose employees and former employees anonymously analyse companies and their management, the Data Scientist profession has become, since the beginning of the year, the most requested within their periodic ranking, with over 4,000 requests from companies and with an average salary around $110,000 per year.

 

The importance of skill-sets

Over the last twenty years, the world has entered a new globalisation phase, where countries and workers have to confront new challenges and opportunities. In this new context, skill-sets have proven to be indispensable in addressing the challenges posed by globalisation, as they can help countries integrate into global markets and specialise in the most technologically advanced industries.

In fact, according to the recent OECD report, countries that recorded the greatest increase in global value chains in the period between 1995 and 2011, benefited from additional annual growth in industrial productivity. This additional growth varies from 0.8 percentage points in industry sectors with a lower production fragmentation potential, to 2.2 percentage points in sectors with more potential, such as for example numerous high-tech manufacturing industries. When the development of expertise goes hand in hand with participation in global value chains, countries can achieve increased growth in productivity. In order to integrate and grow within global markets, all industrial sectors need workers who, in addition to strong cognitive skills, have in particular, skills in literacy, mathematics (numeracy), problem solving, management, communication and the willingness to learn.

Skills needed for the technologically advanced sectors

Unfortunately too many adults still do not have the skills needed to face the challenges posed by globalisation. About one in four adults, or more than 200 million adults in OECD countries, have poor understanding of literacy and mathematics, and over 60% have insufficient skills in both literacy and numeracy. A country with a mix of skills upgraded to the needs of technologically advanced industries can achieve levels of specialisation in these sectors exceeding 10% compared to other countries.

To specialise in the most technologically advanced industries, according to the OECD study, countries need workers with good social and emotional skills such as management, communication and self-organisation, which integrate with cognitive skills. Many technologically advanced industries require workers to complete long sequences of tasks; insufficient performance at any stage in the production sequence reduces production value. On average, countries whose workers have upgraded skill-sets can achieve levels of specialisation in technologically advanced industrial activities that are 2% above those countries with lesser levels of expertise.

The job market and opportunities offered by data skill-sets

The technological sector continues to dominate the panorama of the most requested high-paying roles. The Careers Glassdoor site recently published the annual list of the 50 best jobs, basing the ranking on employee satisfaction, salary potential, and number of current jobs, weighting each factor equally. A Data Science profile develops very quickly, earning positions on the job market to the extent that Big Data, data analysis, data mining and machine learning become more and more relevant to the IT industry. The top 10 places in the rankings contain all the professions related to Analytics, Big Data and Data Science.

The profiles most sought-after by the market

A Data Scientist is the job title used for a consultant or employee of a Business Intelligence (BI) who excels in analysing data, especially large volumes of data, to help a business gain a competitive edge on the market. Ideally the Data Scientist is an “all-rounder” for all data science-related activities and markets, and is responsible for designing and implementing processes used for modelling, data mining, and research. At the same time, he contributes to the development of data mining architectures, standardisation models, reporting and data analysis methods. Research is an integral part of the activities carried out by this professional. In addition to working closely with application developers to create data definitions for new databases and extracting relevant data for analyses. One of the most important skills for a Data Scientist is undoubtedly the ability to explain the meaning of data in a way that can be easily understood by others.

According to PayScale.com, being a Data Scientist in the United States today is a highly profitable career with an average annual salary of $ 91,588. A top-level professional with extensive industry experience can earn up to $148,000 a year, making this professional profile one of the highest and most well-paid roles across the entire IT sector. Diverse skills are required for this professional profile. In particular:

  • Extensive knowledge of programming languages ​​based on data such as R, SAS, Python, SQL, Hive, Spark etc.;
  • Consolidated experience in distributed computing and statistical modelling;
  • Expertise in database architecture, process management, and data modelling, extraction and analysis;
  • Working knowledge of various data analysis, visualisation and reporting tools;
  • Solid grounding in mathematical and statistical preparation;
  • Research-oriented mentality;
  • Mentoring skills to provide guidance to junior team members (data engineers, analysts and statisticians).

Also, according to the recent CareerCast report, the Data Scientist will be the most requested role in 2017. In order to determine the most requested professions, the database evaluates the data of the Bureau of Labor Statistics (BLS) on growth prospects as well as trends within industrial and professional recruitment over the last decade, commercial statistics and university employment data, in order to determine the factors that define recruitment needs.