Description

You will develop and deliver analytics models for equipment condition monitoring, focusing on failure prediction, remaining life estimation, and component risk models.

Responsibilities

• Collect and negotiate requirements for condition monitoring models.
• Access, cleanse, and preprocess time-series machine sensor data, telematics, and service records to cultivate ground truth datasets.
• Develop, backtest, and validate machine learning, deep learning, and statistical models in Python across on-prem and AWS environments.
• Compute model performance metrics and explain modeling decisions to stakeholders.
• Support models in production and resolve incident tickets.

Required Skills

• Python proficiency with NumPy, Pandas, Matplotlib, and Scikit-learn.
• SQL and experience with database APIs including cx_Oracle, PyMySQL, and SQLAlchemy.
• Version control using Git.
• Cloud deployment experience using AWS (S3, EC2, SageMaker) and containerization.
• Experience working within Agile/Scrum environments.
• Bachelor's degree in Data Science, Analytics, Computer Science, Engineering, or a related field with 2-4 years of experience (or a Master's/PhD with 1-2 years of experience).
• Strong critical thinking and independent problem-solving abilities.
• Effective communication skills for collaborating with teams and defending technical decisions.

Preferred Skills

• Machine learning model development and multiprocessing frameworks.
• Familiarity with engineering/IoT systems and telematics/time-series data analysis.
• AWS Certified Cloud Practitioner or higher.

Key Skills

Education

Any Graduate