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

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