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.
- 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