Responsibilities

• Develop and implement machine learning models focused on anomaly detection in
  industrial IoT data.
• Design algorithms for predictive maintenance and condition monitoring.
• Work closely with data engineers to create robust data pipelines for real-time analytics.
• Analyze large datasets, extract insights, and provide actionable recommendations.
• Optimize models to improve detection accuracy and reduce false positives.
• Stay updated on the latest research and advancements in anomaly detection
  techniques.
  Qualifications
• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 5+ years of hands-on experience in machine learning, with a focus on anomaly
  detection.
• Strong expertise in time-series analysis, clustering, and anomaly detection techniques.
• Proficiency in Python, R, or similar programming languages.
• Experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Knowledge of IoT data and predictive maintenance is a strong plus.
• Excellent analytical and problem-solving skills.
  Preferred Skills
• Experience with unsupervised learning techniques for anomaly detection.
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud).
• Background in condition monitoring or predictive analytics for industrial applications.