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Remote role, metro location, and broad ML/toolkit requirements drive high candidate competition.
Industrial sensor/process focus and predictive maintenance domain require industry-specific experience.
Mandatory 8+ years, deep learning and industrial ML experience plus MLOps tooling make filters strict.
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Design, build, and deploy advanced machine learning models for industrial applications such as predictive maintenance, process optimization, and anomaly detection.
Work with large-scale structured and unstructured industrial data from sensors, machines, and operational platforms to develop predictive and prescriptive solutions.
Collaborate cross-functionally with engineers, domain experts, and business stakeholders to deliver scalable, production-grade ML solutions and monitor their performance continuously.
Masters or PhD in Data Science, Computer Science, Statistics, Mathematics, or related field.
Minimum 8 years of professional experience as a Data Scientist, Machine Learning Engineer, or similar role.
Strong experience with deep learning frameworks (TensorFlow, PyTorch) and proficiency in Python, SQL, and data visualization tools.
Experience with industrial, manufacturing, energy, logistics, process, or IoT data and expertise in model deployment, MLOps, and cloud platforms (Azure).
Has strong domain experience with industrial machine learning use cases including predictive maintenance, digital twins, and sensor analytics.
Experienced in end-to-end ML model lifecycle: data pipelines, feature engineering, deployment, monitoring, and optimization in production environments.
Able to translate complex industrial challenges into data science problems and communicate insights effectively to both technical and non-technical stakeholders.