D&I Data Scientist

The Data Scientist is responsible for conducting undirected research and tackle open-ended data problems and questions. 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. Responsibilities * Research and assess next-generation technologies for machinery diagnostics and prognostics and data-driven modeling and optimization of complex systems. * Demonstrate advanced working knowledge and experience with machine learning algorithms and population-based meta-heuristic optimization methods. * Generate innovative ideas, establish new research directions, and shape and execute on technical projects. * Maintain state-of-the-art knowledge and contribute to technical discussions and reviews as an expert in related areas of responsibility. * Apply theoretical knowledge to solve industrial problems. * Process large multivariate data sets collected from equipment operations, manufacturing tests and diagnostic routines. * Apply engineering knowledge in developing data-driven algorithms for anomaly detection, failure prediction and optimization. * Communicate ideas, plans and results effectively via oral and written reports. * Collaborate with field and product engineers to identify key health monitoring parameters of a system. Experience level: 3-12 years Expertise: * Knowledge 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). * Practical experience in the machine learning lifecycle, from problem formulation and data acquisition to model building and deployment at enterprise scale. * Conceptual and mathematical understanding of algorithms, models, model assessment techniques, solution development and explainable AI. * Knowledge and experience in code design, testing, and ML Ops practices. * Specialization in at least one sub-domain, such as time series forecasting, Bayesian skills or NLP-GenAI. * Proficiency in Python, including packages such as NumPy, pandas, scikit-learn, Keras, TensorFlow, and PyTorch. * Experience with software engineering practices, agile methodologies, DevOps, and version control. * Experience in Data and Cloud environments along with APIs and data integration * Experience with GenAI/LLMs