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Data Scientist – Tribology & Advanced Analytics - INNOVATIQ TECHNOLOGIES PTE. LTD.

Data Scientist – Tribology & Advanced Analytics - INNOVATIQ TECHNOLOGIES PTE. LTD.

INNOVATIQ TECHNOLOGIES PTE. LTD.D14 Geylang, Eunos, SG
6 days ago
Job description

Roles & Responsibilities

Key Responsibilities :

  • Apply data science and machine learning techniques to model, simulate, and optimize the behavior of lubricants, dispersants, and related materials.
  • Collaborate with R&D, Engineering, and Product teams to uncover insights from experimental, simulation, and operational datasets.
  • Develop, validate, and deploy predictive models using Azure Machine Learning, supporting product development and process optimization.
  • Translate tribological and physico-chemical domain knowledge into interpretable machine learning features and modeling strategies.
  • Perform exploratory data analysis (EDA) to identify trends, anomalies, and correlations in large-scale, multi-source datasets.
  • Build and maintain pipelines and models for continuous learning and performance monitoring in production environments.
  • Contribute to the development of scientific publications, patents, or internal knowledge bases.
  • Support data governance, data quality, and reproducibility of scientific modeling efforts.

Required Qualifications :

  • MSc or PhD in Tribology, Chemical Engineering, Materials Science, Mechanical Engineering, or a related field with strong data science exposure.
  • Solid understanding of lubricants, dispersants, and their chemical / physical mechanisms.
  • 3+ years of experience in data science, machine learning, or computational modeling roles.
  • Proficiency in Python and key data science libraries (e.g., scikit-learn, pandas, NumPy, matplotlib).
  • Hands-on experience with Azure Machine Learning (AML) – model training, deployment, versioning, and monitoring.
  • Familiarity with statistical modeling, optimization, and time-series or multivariate analysis.

    • Preferred Qualifications :

  • Experience working with sensor data, lab data, or simulation data in a tribological or chemical context.
  • Knowledge of physics-informed machine learning, hybrid modeling, or surrogate modeling techniques.
  • Familiarity with tools like MLflow, Databricks, Azure Data Factory, or Power BI.
  • Strong communication skills, especially in conveying complex technical insights to non-technical stakeholders.

    • Tell employers what skills you have

    Materials Science

    Factory

    Pandas

    Statistical Modeling

    Modeling

    Pipelines

    Azure Machine Learning

    Tribology

    Data Quality

    Data Governance

    Process Optimization

    Publications

    Power BI

    Chemical Engineering

    Matplotlib

    Mechanical Engineering

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    Data Scientist • D14 Geylang, Eunos, SG