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Data Analytics Intern

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Remote, Hybrid

Data Analytics Intern

Henkel

Data Analytics Intern

Skills

SQL QueriesPythonData SciencepandasPyTorchTensorFlowData AnalysisMachine LearningAISAP ERPSupply Chain

14 days ago

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Important information


Contract type:

Permanent

Location:

Singapore

Work mode:

Remote, Hybrid

Published on:

6 October 2026

What they need


About this Position

We are seeking a highly motivated postgraduate intern specializing in Artificial Intelligence, Analytics, Data Science, Machine Learning, or related disciplines to support a strategic Quality 4.0 initiative. The project focuses on developing AI-powered agents that analyze manufacturing and quality data from SAP and the Quality Control Lab Automation & Digitization (QCLAD) platform to generate real-time adjustment material recommendations during production. The objective is to improve process capability, reduce waste, increase first pass quality, and unlock sustainable Day-2 savings opportunities.

What you´ll do

  • Analyze and integrate quality data from Incoming Quality Control (IQC), In-Process Quality Control (IPQC), and Outgoing Quality Control (OQC) systems; Develop AI/ML models to identify process trends and predict quality outcomes.
  • Build AI agents capable of recommending material adjustment proposals during manufacturing operations; Leverage historical SAP production, formulation, and quality datasets to train and validate predictive models.
  • Collaborate with Quality, Manufacturing, Digital, and Data Analytics teams to understand business requirements; Design dashboards and visualizations to demonstrate business impact and savings opportunities.
  • Evaluate agentic AI frameworks and Generative AI approaches for manufacturing decision support.
  • Document methodologies, algorithms, and recommendations for future deployment and scaling.
  • Expected Deliverables are as follows: prototype AI agent for production adjustment recommendations, predictive models correlating IQC, IPQC, OQC, and process parameters with final product quality, quantified Day-2 savings opportunities through reduced rework, scrap, and process variability, and deployment roadmap for scaling the solution across manufacturing sites.

What makes you a good fit

  • Currently pursuing a Master's in Artificial Intelligence, Analytics, Data Science, Computer Science, Industrial Engineering, Operations Research, or related fields at NUS or NTU.
  • Strong knowledge of Machine Learning, Predictive Analytics, Statistical Modeling, and Generative AI concepts.
  • Experience with Python, SQL, and data science libraries (Pandas, Scikit-Learn, PyTorch, TensorFlow, etc.).
  • Familiarity with LLMs, AI agents, Retrieval-Augmented Generation (RAG), or agent orchestration frameworks is highly desirable.
  • Understanding of manufacturing, process engineering, quality systems, or supply chain analytics is a plus.
  • Strong problem-solving skills and ability to translate business challenges into analytical solutions, along with strong collaboration and interpersonal skills.

Some perks of joining Henkel

  • Flexible work scheme with flexible hours, hybrid work model, and work from anywhere policy for up to 30 days per year
  • Diverse national and international growth opportunities
  • Global wellbeing standards with health and preventive care programs
  • Gender-neutral parental leave for a minimum of 8 weeks
  • Employee Share Plan with voluntary investment and Henkel matching shares
  • Outpatient Medical and Specialist Coverage for employees and eligible dependents
  • Life, Hospital, Personal Accident Insurance Coverage
  • Flexible Benefits covering Dental, Vaccination, Eye screenings etc for employees and eligible dep
  • Access to company products at discounted price

At Henkel, we come from a broad range of backgrounds, perspectives, and life experiences. We believe the uniqueness of all our employees is the power in us. Become part of the team and bring your uniqueness to us! We welcome all applications across different genders, origins, cultures, religions, sexual orientations, disabilities, and generations.

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