Data Scientist

Based in Tulsa, OK, Vital Energy stands as a paragon of innovation within the upstream oil and gas sector. As a nexus between state-of-the-art technology and energy resource management, we have transformed the paradigm of operational efficacy through our innovative approaches. Not just a name, but a movement within the industry, Vital Energy epitomizes cutting-edge innovation, setting standards, and leading the charge with solutions that chart the future.

Role

Description:

Data Scientist I

As an entry-level Data Scientist I at our oil and gas company, you will play a crucial role in supporting our data-driven decision-making processes. Your main responsibilities will include:

• Collecting, cleaning, and preprocessing data from various sources:
This includes sensors, logs, and databases.
• Building and implementing machine learning models:
This involves predicting and optimizing key performance indicators related to oil and gas operations. These indicators include production efficiency, equipment maintenance, and resource allocation.
• Creating clear and insightful data visualizations and dashboards:
This involves communicating findings and insights to non-technical stakeholders.
• Collaborating with cross-functional teams:
This includes engineers, geologists, and business analysts, to identify opportunities for data-driven improvements in operational processes.
• Staying up-to-date with industry trends and advancements in data science:
This includes leveraging this knowledge to drive innovation within the organization.
• Maintaining thorough documentation:
This includes data sources, methodologies, and model development to ensure transparency and reproducibility.
• Actively seeking opportunities:
This involves enhancing the efficiency and effectiveness of data analysis processes.

At Vital Energy, you will be more than just a Data Scientist. You’ll be at the heart of a team shaping the future of the oil and gas industry. Step into our world in Tulsa, and together, let’s revolutionize the energy landscape.

Technical Qualifications

• Education: Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Programming Skills: Proficiency in Python or R for data analysis and machine learning.
• Machine Learning: Basic understanding of machine learning algorithms and statistical analysis techniques.
• Data Manipulation: Proficiency in data manipulation libraries such as pandas and Num Py.
• Problem-Solving: Strong problem-solving skills with attention to detail.
• Communication: Excellent communication skills to convey complex technical concepts to non-technical stakeholders.
• Team Collaboration: Ability to work collaboratively in a team-oriented environment.
• Adaptability: Eagerness to learn and adapt to new challenges in a rapidly evolving field.
• Big Data: Familiarity with big data technologies, especially within the AWS ecosystem.
• Data Extraction:

Experience with data extraction and transformation from unstructured sources.
• Cloud Computing: Knowledge of cloud computing platforms, specifically AWS, including AWS Lambda, S3, Glue, and Sage Maker.
• Geospatial Data: Understanding of geospatial data analysis techniques.
• Relevant Experience: Previous internships or project work in data science or analytics.

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