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Data Scientist

27 days ago 2024/12/22
Full Time Employee
1-9 Employees · Human Resources Outsourcing

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Job Description

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.


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