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.

Skills

Required Skills & Qualifications:

• Bachelor's or Master’s degree in Data Science, Statistics, Computer Science, or a related field.
• 5-7 years of hands-on experience in data analysis, machine learning, and statistical modeling.
• Proficiency in programming languages such as Python and R.
• Expertise in machine learning algorithms, natural language processing, and deep learning.
• Solid understanding of statistical techniques such as regression analysis, clustering, forecasting, and ARIMA models.
• Experience with big data technologies (e.g., Hadoop, Spark) and database management (SQL, NoSQL).
• Strong analytical skills and ability to identify and extract meaningful insights from complex datasets.
• Familiarity with data visualization tools and techniques.
• Hands-on experience with Seeq Workbench, Organizer, and Datalab for advanced data analysis.
• Ability to work independently and as part of a cross-functional team.
• Strong communication skills to effectively explain technical concepts to non-technical stakeholders.

Preferred Qualifications:

• Experience in working with industrial data, specifically related to equipment such as compressors.
• Knowledge of asset management and fault diagnosis techniques.
• Previous experience in a similar role within industries like manufacturing, oil and gas, energy, or utilities.