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Machine Learning Researcher
Job in
Warren, Macomb County, Michigan, 48091, USA
Listed on 2025-12-07
Listing for:
Optimal Staffing
Full Time
position Listed on 2025-12-07
Job specializations:
-
Engineering
-
IT/Tech
Data Scientist, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Operations Manager - Talent Acquisition at Optimal, Inc.
We are seeking a highly skilled Machine Learning Engineer with a strong background in predictive modeling, data science, statistics, and industrial & operational engineering (IOE). Predictive modeling and correlation analysis of body dimensional data. 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.
- PhD in Industrial & Operations Engineering, Mechanical Engineering, Manufacturing 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, Tensor Flow, PyTorch, etc.).
- Strong analytical, problem-solving, and communication skills.
- 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.
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.
- Associate
- Contract
- Information Technology and Research
- Motor Vehicle Manufacturing
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