Drilling rates, seismic data, shipping and transportation rates, and occupational safety statistics are just a small sample of the large amounts of data that energy companies generate every day. But, what can they do with all that data? That’s where data scientists come in. They have a knack for connecting the dots to find useful insights and realize the data’s full potential.
The process of exploring and developing energy resources generates vast amounts of data. In oil and gas companies, data scientists mine this data to glean useful insights from a wide variety of sources ranging from explorations, well drilling and production sensors. Data mining combines statistics, artificial intelligence (AI) and database research.
Data scientists analyze and profile large, complex, multi-dimensional datasets using a variety of tools. This analysis is used to find patterns, anomalies, and optimization opportunities as well as provide predictive analytics for the company’s consideration. There is a significant emphasis on research, design and implementation of cutting-edge algorithms and models to ensure the company’s data analysis benefits from new developments in data science. The Data Scientist also provides expertise and advocacy about data science processes and solutions, communicating insights and learnings to colleagues so these are understood and implemented in the broader business decisions within a company.
In large companies, data scientists typically lead analytic practices in cross-functional teams to identify and prioritize actionable and impactful insights across core business areas.
I'm interested in a career in
- Sub-sector Exploration and production, Oil and gas services, Pipelines
- Environment Primarily indoor/office work
- Average Salary $60,000 to $116,000
- Education Post-secondary degree
- Career Demand Stable
In this occupation activities may include:
- Proficiency with analytics scripting languages such as Python, R, SQL and statistical analysis environments such as MATLAB, SPSS or SAS
- Experience with lamda architectures and batch and real-time data streams
- Experience in industry data science (e.g., machine learning, predictive maintenance)
- Experience with agile or other rapid development methods
- Experienced in object-oriented design, coding and testing patterns as well as experience in engineering software platforms and large-scale data
- The typical minimum academic requirement is a bachelor’s degree in computing science, statistics, actuarial sciences, statistics, mathematics or computer engineering.
- Certification is not required, as there is currently no legislation regulating this occupation.
- Minimal or no travel
- Primarily indoor/office work
- Work not physically demanding
You are consistently working to make things run better or more efficiently. You can transform a sea of data into actionable insights-from finding better ways to schedule deliveries to discovering ways to optimize a reservoir.â€¯Combining your knowledge of computer science, statistics, and analytics, you can find answers that will help organizations make objective decisions.
- Computers and Electronics
- Customer and Personal Service
- Information and Document Management
- Cyber/Data Security
- Understanding Risk
- Interpreting Documents/Plans
- Complex Problem Solving
- Cost Benefit Analysis