Who you are

• Strong foundational knowledge in machine learning, data science, and statistical modeling
• Familiarity with time-series modeling techniques and feature engineering
• Experience in deploying machine learning models on real-world use cases and continuously improving the model performance with feedback
• 3+ years of proven programming skills using standard ML tools such as Python, PyTorch, Tensorflow etc
• Master’s or Ph.D. in Computer Science, Data Science, Mechanical Engineering, Electrical Engineering, or a related field with a focus on condition monitoring or machine learning applications
• Hands-on experience developing models for OT and vibration analysis, condition monitoring, and fault detection or classification
• Familiarity with signal processing techniques (e.g., Fourier transforms, wavelet analysis) and their application to OT and vibration data

What the job involves

• As we enter our next phase of growth, we’re investing deeply in AI/ML, LLMs, and Industrial IoT to transform how frontline teams operate—predicting failures before they happen, automating workflows, and embedding intelligence into every asset and procedure
• Develop and train machine learning models for fault detection and classification based on time-series sensor data; including vibration, temperature, pressure, flow etc
• Perform exploratory data analysis (EDA) on vibration, OT and time-series data to uncover insights and identify patterns indicative of faults or anomalies
• Experiment with and evaluate various algorithms, including time-series modeling, signal processing, and statistical methods, to optimize model performance
• Collaborate with domain experts to validate findings and ensure alignment with real-world applications
• Document workflows, experiments, and methodologies to ensure reproducibility and knowledge sharing across the team
• On-call duties