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.

Required Skills & Qualifications

• Programming: High proficiency in Python (specifically Pandas for data manipulation and NumPy for numerical computing).
• Database Management: Ability to write SQL queries to extract data from relational databases.
• Statistical Foundation: Understanding of probability, hypothesis testing, and statistical significance.
• Machine Learning: Familiarity with ML libraries like Scikit-Learn.
• Education: Currently pursuing or recently completed a degree (BS/MS) in Data Science, Statistics, Computer Science, Computer Engineering, Mathematics, or a related field.

Bonus

• Basic exposure to LLMs or Prompt Engineering (e.g., using OpenAI or LangChain APIs).
• Creating Dashboards and visualizations (ex. PowerBI)