About the Role
The role requires a strong focus on data analysis, machine learning model development, and fraud detection across large-scale datasets. The ideal candidate collaborates closely with engineering and product teams to build scalable and reliable machine learning solutions that support data-driven decision-making. Exposure to model development, feature engineering, experiment tracking, and modern MLOps practices is a strong advantage.
Key Responsibilities
- Design, develop, and refine high-performance Fraud Prevention models using Python and Gradient Boosting frameworks such as XGBoost, LightGBM, or CatBoost.
- Manage the complete machine learning lifecycle including data extraction, feature engineering, model training, evaluation, and deployment support.
- Conduct data research, behavioural analysis, and performance benchmarking on production datasets.
- Write and optimize SQL queries to extract and analyse data from PostgreSQL databases for model development and validation.
- Utilize MLflow for experiment tracking, model versioning, and ensuring reproducibility across development stages.
- Maintain code integrity and collaborative workflows using Git and Bitbucket.
- Work within Linux environments and utilize shell scripting (Bash) to automate workflows and operational tasks.
- Develop visualizations and analytical insights using data visualization tools.
- Collaborate with cross-functional teams to improve model performance and data-driven decision making.
- Ensure data privacy, security, and compliance best practices while working with production data.