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Drawing on an advanced degree in a quantitative field such as computer science, physics, statistics or applied mathematics, the Data Scientist demonstrates the knowledge to invent new algorithms to solve data problems.\nResponsibilities\nResearch and assess next-generation technologies for machinery diagnostics and prognostics and data-driven modeling and optimization of complex systems.\nDemonstrate advanced working knowledge and experience with machine learning algorithms and population-based meta-heuristic optimization methods.\nGenerate innovative ideas, establish new research directions, and shape and execute on technical projects.\nMaintain state-of-the-art knowledge and contribute to technical discussions and reviews as an expert in related areas of responsibility.\nApply theoretical knowledge to solve industrial problems.\nProcess large multivariate data sets collected from equipment operations, manufacturing tests and diagnostic routines.\nApply engineering knowledge in developing data-driven algorithms for anomaly detection, failure prediction and optimization.\nCommunicate ideas, plans and results effectively via oral and written reports.\nCollaborate with field and product engineers to identify key health monitoring parameters of a system.\nExperience level: 3-12 years\nExpertise:\nKnowledge and practical experience in statistical analysis techniques (e.g., classification, regression, time-series, Bayesian techniques) and machine learning techniques (e.g., decision trees, ensemble methods, deep learning, neural networks, validation methods).\nPractical experience in the machine learning lifecycle, from problem formulation and data acquisition to model building and deployment at enterprise scale.\nConceptual and mathematical understanding of algorithms, models, model assessment techniques, solution development and explainable AI.\nKnowledge and experience in code design, testing, and ML Ops practices.\nSpecialization in at least one sub-domain, such as time series forecasting, Bayesian skills or NLP-GenAI.\nProficiency in Python, including packages such as NumPy, pandas, scikit-learn, Keras, TensorFlow, and PyTorch.\nExperience with software engineering practices, agile methodologies, DevOps, and version control.\nExperience in Data and Cloud environments along with APIs and data integration\nExperience with GenAI/LLMs","description_format":"text","description_chars":2470,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-01T21:12:22Z"}],"visa":[],"liveness":{"score":53,"band":"ok","label":"Likely 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