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Mid-level ML role, broad skillset and metro context create high applicant competition.
Role requires specific ML/DL and production deployment expertise, limiting cross-industry transferability.
Explicit Master's degree, 2+ years experience, and many mandatory ML/MLOps skills increase strictness.
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Design and develop ML/AI algorithms to solve power management problems, including integration into edge or cloud systems via CI/CD and software release.
Deliver architecture, technical deliverables, and project outcomes through the entire project lifecycle using Agile methodologies.
Collaborate with experts across deep learning, machine learning, distributed systems, and product teams to develop end-to-end pipelines and intelligent power technology products.
Master’s Degree in Data Science.
Minimum 2 years of practical data science experience applying statistics, machine learning, and analytic approaches.
At least 2 years experience delivering technology solutions in a production environment.
Experience working direct with customers for requirements gathering and solution architecture.
Strong background in statistical methods such as Bayesian networks, hypothesis testing, and optimization techniques.
Hands-on experience developing ML/DL models for engineering applications (electrical/electronic systems, energy, mechanical systems) and working knowledge of programming languages like Python, R, Matlab, C/C++, Java.
Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn), MLOps, cloud platforms (Azure ML, Databricks), and Agile tools, capable of driving solutions in global, cross-functional teams.