About the Research
The data innovation team conducted a comprehensive survey of data scientists in the oil and gas industry to understand their current needs and expectations. The research covered over 120 profiles and included 10 interviews, making it the most extensive study conducted by the department. The study targeted six companies currently investing in their data science teams: Conoco Philips, Evonik, DOW, BASF, ExxonMobil, and Equinor. The research spanned 16 countries, from North America to Latin America, Europe to Asia.
Key Findings
The research identified three main categories of data scientists in these companies: Computer Science, Chemical, and Research. Despite the growing demand for data scientists, they still represent a small number of employees within the companies they work for, averaging 0.52% of the workforce.
Traits of Data Scientists
Data science teams are a relatively new phenomenon in the process industry, usually related to digital transformation initiatives. The research revealed that data scientists in the process industry come from diverse backgrounds and career paths. Many come from Masters and Ph.D. programs in fields ranging from Engineering to Astrophysics and Social Sciences. Others come from many new Data Science graduate programs. The most common path is still from other technology backgrounds, such as Computer Science and Chemical Engineering.
Tools and Environment
Data scientists typically use a similar set of development tools to build analysis and machine learning models. Most of them use Python with modern open-source libraries, an infrastructure (on-premise or on the cloud), and a variety of data sources. Two trends were consistent across multiple interviews: the idea of a one-stop-tool is unrealistic, and the fact that familiar tools are a critical component to drive adoption.
Profiles Analysis
The research provides a comprehensive understanding of the data scientist profile in the process industry. The findings will guide the AioT leadership to ensure that future investment in the Data Scientist persona provides maximum returns. The research also highlights the need for tools and environments that support the unique needs and workflows of data scientists in the process industry.
Trends and Insights
The research provides a comprehensive understanding of the data scientist profile in the process industry. The findings will guide the AioT leadership to ensure that future investment in the Data Scientist persona provides maximum returns. The research also highlights the need for tools and environments that support the unique needs and workflows of data scientists in the process industry.
Conclusion
The research provides a comprehensive understanding of the data scientist profile in the process industry. The findings will guide the AioT leadership to ensure that future investment in the Data Scientist persona provides maximum returns. The research also highlights the need for tools and environments that support the unique needs and workflows of data scientists in the process industry.