As in every industry today, the Oil and Gas industry seeks ways to improve efficiencies, reduce operating costs and increase revenues. Data science and business analytics experts should be able to offer exemplary solutions that, when embraced by the industry, will be instrumental in improving efficiency. Unlike many other industries, Oil and Gas organizations face unique safety, environmental, and regulatory reporting requirements. Data scientists working for these organizations should be well aware of such requirements.
Required Skills
- Working experience in data science projects related to oil, gas, or energy sectors
- Proficient in Python
- Expert in geospatial models
- Extensive experience with applying statistical methods to solve complex real-world problems
- Extensive experience with scikit-learn, statsmodels, SciPy
- Extensive experience in linear programming and matrix algebra
- Extensive experience with probability models
- Extensive experience with optimization methods
- 1+ years of experience with Geographic Information Systems (GIS)
- 1+ years of experience in dealing with GPS, location sensors, social media, mobile devices, and satellite imagery as data
- Expert in segmented into the surface analysis, network analysis, and geovisualization
- Extensive experience with ArcGIS
- Extensive experience with Arcpy, geopandas, shapely, folium, GDAL/OGR, RSGISLib, PyProj, PySAL, scikit-learn, statsmodels, and SciPy
- Expert in supervised (classification, regression) and unsupervised machine learning (anomaly detection, cluster analysis)
- Hands-on experience writing data processing and data pipeline for unstructured data model development including gathering and building datasets to collect intents, cleaning noisy data, designing feedback loop on data needs
- Proficient in SQL
Preferred Skills
- Experience with NoSQL databases
- Experience with BigQuery
- Experience with R/Scala
- Experience with Containers
- Experience with Django
- Experience with active learning and reinforcement learning
- Experience with GCP, AWS, or Azure
- Experience with Big Data platforms such as Spark (SparkML/PySpark)
