Machine Learning Engineer - APAC

Machine Learning Engineer

Job Summary

This role directly supports our strategic shift toward machine learning, predictive analytics, and Agentic development in the region, enabling faster and more reliable delivery of insights and outcomes for APAC stakeholders on our modern data platforms.

Responsibilities

• Data Processing at Scale . Clean, transform, and join raw datasets, handling missing data, outliers, normalization, and leakage prevention using SQL and Python .

• Design and develop ML models tailored to business needs, leveraging statistical methods to ensure accuracy and reliability. Apply classical and modern techniques including regression, classification, time series analysis, and hypothesis testing to build trustworthy models.

• Implement ML algorithms with an emphasis on performance and interpretability.
  Select appropriate algorithms and use statistical techniques to optimize hyperparameters, reduce variance and bias, and manage class imbalance.

• Conduct disciplined experiments to test and validate models. Design experimental frameworks, use train validation test splits and cross validation, and interpret results with appropriate statistical significance and confidence intervals.

• Feature engineering rooted in business and statistical understanding. Create informative features through aggregation, encoding, interaction terms, and time windows; assess feature importance and stability over time.

• Model evaluation using statistically sound metrics. Evaluate with precision, recall, F1 score, ROC AUC, calibration, confusion matrices, and cost sensitive metrics appropriate to the problem.

• Collaborate with data scientists to embed statistical insights into model design and validation, ensuring robust predictive analytics and practical deployment pathways.

• Optimize and productionize models for reliability and speed. Tune hyperparameters, apply regularization and ensembling, implement monitoring for drift and performance, and manage A/B rollouts on Databricks and related tooling.

• Reporting and documentation that clearly communicates methodology, assumptions, statistical analyses, and business implications to technical and non-technical stakeholders.

• Agentic creation for intelligent solutions that designs and implements autonomous, adaptive workflows using Agentic development principles to enable self-directed decision-making and dynamic integration across business processes.

• Workflow Automation and Optimization which develops and refines automated pipelines for data processing, model deployment, and monitoring, leveraging tools such as Databricks and Microsoft Fabric to ensure scalability, efficiency, and minimal manual intervention.

  Requirements

• Master s degree preferred, with a minimum of a Bachelor s degree in Data Science, Statistics, Computer Science, or a related field.

• Five or more years of experience in data and machine learning or closely related roles, demonstrating independent execution of best practices and end to end delivery from development and testing through production.

• Demonstrates expertise in our technology stack (Databricks, Microsoft Fabric, and Power BI) to support platform engineering activities, including operating, maintaining, and providing break/fix coverage for core data platforms.

• Excellent communication skills with the ability to translate technical and statistical concepts into clear, actionable insights for both technical and non-technical stakeholders.

• Ability to work effectively with cross-functional teams across regions, fostering collaboration and knowledge sharing.

• Support and encourage a high-performing team culture where treating everyone with respect is a core expectation, fostering inclusivity, trust, and accountability in all interactions.

• Must be able to hold technical conversations across SQL, Python, Data Modelling Evaluations, Statistical Foundations, and ML Algorithms during technical interview.

• Experience in the following areas will be highly advantageous:

  • NLP (text preprocessing, topic modeling, classification, sentiment analysis)
  • Agentic models Generative AI
  • Databricks
  • Microsoft Fabric (model administration maintenance)
  • Workflow automation
  • ETL pipelines
  • PowerApps
  • MLflow
  • Data pipeline orchestration
  • Version control
  • CI/CD
  • Model monitoring
  • Power BI

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