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Data Scientist

Business Unit:  Research & Development
Division:  Technology Innovation Group
Department:  AIR Center

Your Mission

This position is responsible for applying data-driven problem-solving, data science, and optimization techniques to resolve real-life manufacturing problems and deploy the ideated solutions in a production environment.

What To Expect

HMGICS is seeking data scientists with a deep interest in solving real-life industrial problems and applying their solutions at scale in a mass-production environment. Candidates should have a technological interest in at least one of the following domains: Data Analytics, Optimization, AI/ML, or Generative AI & LLMs. Additionally, candidates should have a functional interest in at least one of the following areas: logistics, supply chain, assembly, quality, maintenance, or enterprise functions.


  • Participate as a data scientist in multi-disciplinary project teams aiming to improve the performance of our production environment in specific areas such as logistics and supply chain, assembly, maintenance, and quality.
  • Perform explanatory analytics to understand the key underlying drivers of performance and how to further optimize them.
  • Develop and maintain optimization or predictive models as required to assist production in delivering productivity, flexibility, quality, or sustainability improvements.
  • Deploy these models in production and train engineers and technicians to leverage the insights for improving their day-to-day performance.

What You'll Bring

  • Bachelor's or Master's degree in Industrial Engineering, Computer Science, Mathematics, or equivalent practical experience.
  • 2-5 years of project execution experience as a data scientist, resolving industrial problems across the value chain, including manufacturing, and ensuring the adoption of data science solutions.
  • Preferred experience in the manufacturing and/or automotive industry.
  • Strongly preferred experience in implementing data science and/or AI projects in real-life industrial environments (large POCs or at-scale deployments).
  • Critical end-to-end problem-solving skills, including rapid and rigorous issue analysis, idea development and implementation, and feasibility demonstration and deployment at scale.
  • Experience in applying Regression/Classification/Clustering models, Large scale data analysis, Time series analysis, Forecasting models, or Kernel-based methods.
  • Experience in algorithm development using Mathematical programming (Linear programming, Mixed-integer programming), (Meta)Heuristic algorithms, Stochastic Process, or Combinatorial Optimization.
  • Expertise in processing large-scale datasets in distributed data frameworks (Hadoop, Spark, or Hive).
  • Expertise in data mining frameworks such as PyTorch, TensorFlow, Scikit-learn, or MLlib.
  • Expertise in applying machine learning, Generative AI, LLMs is a plus.
  • Fluent in English (written & spoken).

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