Data Scientist I

Vital Energy; Tulsa (OK); On-site; USD 60,000 - 80,000; Full time

Job summary

Vital Energy seeks an entry-level Data Scientist I in Tulsa, OK, to support data-driven decision-making. You will collect and preprocess data, build machine learning models, and collaborate with teams to enhance operational efficacy. Join us and contribute to shaping the future of the oil and gas industry.

Qualifications

• Strong programming skills in Python or R.
• Knowledge of machine learning algorithms and statistical analysis.
• Excellent communication skills to convey complex concepts.

Responsibilities

• Collect and preprocess data from various sources.
• Build and implement machine learning models.
• Create visualizations and dashboards for insights.

Skills

Python

R

Machine Learning

Data Manipulation

Problem-Solving

Communication

Team Collaboration

Adaptability

Big Data

Geospatial Data

Education

Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field

Tools

AWS

pandas

NumPy

Job description

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 NumPy.
• 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 SageMaker.
• Geospatial Data: Understanding of geospatial data analysis techniques.
• Relevant Experience: Previous internships or project work in data science or analytics.

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