Job Title: Machine Learning Engineer Start Date: January 2026 (first week) Education Requirement: PhD or Postdoctoral experience strongly preferred. Candidates graduating in December 2025 are welcome to apply. Industrial & Operations Engineering (IOE) / Data Science About the Role We are seeking a highly skilled Machine Learning Engineer with a strong background in predictive modeling, data science, statistics, and industrial & operational engineering (IOE). The ideal candidate will be creative in building innovative, data-driven models and capable of applying advanced analytical techniques to solve complex, real-world problems. Key Responsibilities Develop, implement, and optimize machine learning models for predictive analytics and decision support. Apply statistical methods and data-science techniques to large, complex datasets. Build scalable data-driven modeling frameworks and analytical tools. Collaborate with cross-functional teams to translate engineering and operational challenges into ML-based solutions. Communicate technical results clearly to both technical and non-technical audiences. Stay current with emerging research and trends in ML, statistics, and IOE. Required Qualifications PhD in Machine Learning, Data Science, Statistics, Computer Science, Industrial & Operations Engineering, or a related field. Postdoctoral experience preferred, but not required. Candidates completing their PhD by December 2025 will be considered. Strong knowledge of predictive modeling, statistical analysis, and data science methodologies. Demonstrated experience with creative modeling approaches for data-driven solutions. Proficiency in ML tools and languages (Python, R, TensorFlow, PyTorch, etc.). Strong analytical, problem-solving, and communication skills. Preferred Skills (Nice to Have) Experience with industrial systems, optimization, or operations research. Publications in relevant ML, data science, or engineering conferences/journals. Experience working with real-world operational datasets. Interview Process Total Duration: ~1 hour Presentation (20-30 minutes): Candidate will present an academic project, dissertation work, or relevant work from previous employment. Q&A Session: Open discussion with the interview panel following the presentation.