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NAVA Software solutions is looking for a Data Scientist

Details:

Data Scientist

Location: Houston TX Remote is ok

Duration: 12 months

Clients want a data scientist who can develop machine learning models to run forecast scenarios. Also, they want this person to be knowledgeable in AWS Cloud technology

A Data Scientist with physical pipeline experience typically specializes in analyzing and optimizing physical infrastructure pipelines, such as those used in the oil and gas industry or transportation networks. Here are some common job duties associated with this role:

• Data collection and integration: Data scientists with physical pipeline experience gather data from various sources related to the infrastructure pipelines, such as sensors, SCADA (Supervisory Control and Data Acquisition) systems, or IoT devices. They integrate and consolidate the data for analysis and modeling.
• Pipeline performance analysis: These professionals analyze the performance of physical pipelines by examining data related to flow rates, pressure levels, temperature, corrosion, and other relevant factors. They use statistical techniques and machine learning algorithms to identify patterns, anomalies, and potential issues that may affect pipeline operations.
• Predictive modeling and maintenance optimization: Data scientists develop predictive models to forecast pipeline performance and detect potential failures or maintenance needs. They utilize historical data, sensor measurements, and other relevant parameters to train models that can predict future events, such as leaks, blockages, or equipment failures. By identifying critical maintenance requirements in advance, they can optimize maintenance schedules and minimize downtime.
• Risk assessment and mitigation: Data scientists assess risks associated with physical pipelines, such as environmental hazards, security threats, or regulatory compliance. They develop risk assessment models and analyze the impact of different factors on pipeline safety and integrity. Based on these analyses, they propose mitigation strategies to minimize risks and ensure compliance with safety regulations.
• Optimization of pipeline operations: Data scientists work on optimizing the operational efficiency of physical pipelines. They analyze data to identify areas of improvement, such as reducing energy consumption, optimizing transportation routes, or improving overall system performance. By applying data-driven approaches and algorithms, they provide recommendations to optimize pipeline operations and maximize efficiency.
• Visualization and reporting: Data scientists with physical pipeline experience create visualizations, reports, and dashboards to communicate their findings and recommendations effectively. They present complex data in a visually understandable format, allowing stakeholders to make informed decisions regarding pipeline maintenance, operations, and risk management.
• Collaboration with cross-functional teams: These professionals collaborate with engineers, domain experts, operations personnel, and other stakeholders involved in managing physical pipelines. They work together to understand the specific requirements, constraints, and challenges associated with the infrastructure. Effective communication and teamwork are essential to ensure alignment and successful implementation of data-driven solutions.
• Continuous improvement and innovation: Data scientists keep up with the latest advancements in data science, machine learning, and pipeline technologies. They explore new methodologies, algorithms, and tools to enhance their skills and propose innovative solutions to address pipeline-related challenges.