The Role
Conduct open-ended data science research and develop machine learning algorithms for machinery diagnostics, prognostics, anomaly detection, failure prediction, and system optimization. Process large multivariate equipment and manufacturing datasets, apply statistical and engineering knowledge, and research advanced technologies. Build and deploy enterprise-scale models using MLOps practices, collaborate with field and product engineers, establish research directions, and communicate technical findings.
Summary Generated by Built In
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
Skills Required
- Advanced degree in computer science, physics, statistics, applied mathematics, or another quantitative field
- Knowledge and practical experience with statistical analysis, including classification, regression, time-series, or Bayesian techniques
- Knowledge and practical experience with machine learning techniques, including decision trees, ensemble methods, deep learning, neural networks, and model validation
- Experience across the machine learning lifecycle, from problem formulation and data acquisition through model building and enterprise-scale deployment
- Conceptual and mathematical understanding of algorithms, models, model assessment, solution development, and explainable AI
- Knowledge and experience with code design, testing, and MLOps practices
- Specialization in at least one area such as time-series forecasting, Bayesian methods, or NLP and GenAI
- Proficiency in Python and packages including 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, APIs, and data integration
- Experience with GenAI and large language models
- Three to twelve years of relevant experience
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
We are a technology company that unlocks access to energy for the benefit of all. As innovators, that’s been our mission for nearly a century. Today, we face a global imperative to create a future with more energy, but less carbon. Our diverse, innovative change makers are focused on going further in innovation and inventing the new energy technologies we need to get there.
