Why PhDs make good data science professionals


Data science professionals are currently highly sought-after profiles in a large number of companies. Working conditions and salaries are generally very competitive, and the jobs are interesting for those who want to bring meaning to data. Although the sector is still quite young, employers are already looking for candidates with a higher and higher level of seniority. In addition to technical and technological skills, they are also looking for flexible profiles with excellent adaptability, with the aim not only of grasping the rapid changes in the sector but also of deriving interesting business opportunities from them. We had the opportunity to speak with Yotta Academy founder and CEO, Sacha Samama about the skills in demand and the expectations towards candidates. We intend to determine their adequacy with the doctoral skills, which are real assets in these professions.


Which opportunities for PhDs in data?


For several years now, business and tech pundits have been describing data as the new black gold, whether right or wrong. Data Scientist's job was crowned Job of the Year for four consecutive years by the job search and recruitment site Glassdoor in the United States. It remains in the top 10 today. In France, hundreds of job offers appear on LinkedIn for positions such as Data Scientist, Data Analyst, Data Architect and Machine Learning.

"In terms of sectors where demand for data professionals is increasingly strong, one can think of industry, for instance in supply chain management, especially in retail," says Sacha Samama. "There is also a strong interest in these profiles in the various fields of healthcare. We are already starting to see an emergence of specializations at the level of company functions or even sectors of activity." Beyond the buzzwords, data professions offer very concrete career opportunities.


In order to keep (or enhance) their competitive edge, companies large and small are trying to make the shift and become data-driven, i.e. guided by data in their processes, their business decisions, but also in their services or products. As data professions continue to define themselves and mature, Chief Data Officers are taking their place at the top of organizations' hierarchical structures, alongside traditional C-level executives such as Chief Operating Officers (COOs) and Chief Financial Officers (CFOs).


This transition, however, presents significant challenges. Some of them are addressed in the 2019 edition of the "Big-Data and AI Executive Survey", which is conducted by professor and frequent Harvard Business Review contributor Thomas Davenport and Randy Bean, CEO of NewVantage Partner. The survey gathers the responses of 64 data and IT executives from major global corporations (Citigroup, Sanofi, Ford Motors, Credit Suisse, etc.). It shows that only 31% of respondents describe their organization as data-driven. This is a significant drop from the 37.1% reported in the 2017 edition, despite ever-increasing investments in transformation initiatives. Still, only 5% of respondents cite technology issues as a challenge to business transformation, while 40.3% point to a lack of coordination and organizational agility, and 23.6% point to cultural resistance within teams. This suggests that the solution is more likely to be found in the medium to long term; and that there is no easy, ready-made solution.