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Tier-1 brand, metro location, and popular Data Scientist title increase candidate competition.
Core ML skills are transferable but manufacturing/MES expertise makes background fit more specific.
Numerous mandatory technical requirements (Python, TensorFlow, GenAI, cloud, MES) make filters strict.
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Lead end-to-end design, development, and deployment of machine learning models and analytical solutions for global manufacturing and engineering operations.
Collaborate with Smart Manufacturing Strategy Leads to align data science initiatives with business goals across multiple sites.
Mentor data scientists and engineers, provide technical consulting and hands-on support to site teams, including prototype development and validation of use cases.
Proficient in Python, SQL, and cloud-based analytics platforms such as GCP or Snowflake.
Experience with machine learning frameworks like TensorFlow or Keras and strong software development skills with OOP principles.
Familiarity with manufacturing systems (MES), time-series data, image data, anomaly detection, and GenAI applications.
Bachelor’s or Master’s degree in Computer Science, Electrical/Electronic Engineering, Data Science, or related field, or equivalent experience. Work Experience Required: Not explicitly mentioned in the JD.
Experienced in driving end-to-end data science solutions within manufacturing or engineering environments, focusing on operational impact.
Strong technical leadership with ability to oversee globally distributed teams and mentor junior staff.
Skilled in combining advanced machine learning techniques, GenAI productivity tools, and software development practices to deliver scalable analytics solutions.