Data Scientist

Key Responsibilities:

• Analyze and utilize field data to develop predictive and prescriptive asset fault signatures.
• Understand the working principles of industrial equipment, particularly compressors, and identify failure modes, root causes, and control system dynamics.
• Apply advanced statistics and machine learning models to solve business problems and drive data-driven decision-making.
• Conduct statistical modeling (predictive, regression, classification, hypotheses testing, multivariate analysis, time series, clustering, forecasting, ARIMA) using Python or similar tools.
• Implement machine learning models to identify patterns in large datasets and predict equipment performance and potential failures.
• Perform data mining, data preprocessing, feature engineering, and develop advanced analytics and deep learning models.
• Use data visualization techniques to communicate insights and model results to stakeholders effectively.
• Design and conduct experiments to validate models and hypotheses, ensuring their practical application in real-world scenarios.
• Collaborate with cross-functional teams to extract, clean, and analyze relevant data from various databases (SQL, NoSQL, etc.).
• Leverage big data technologies (e.g., Hadoop, Spark) to process and analyze large datasets efficiently.
• Utilize Seeq Workbench, Organizer, and Datalab for data analysis, visualization, and reporting.