Data Scientist

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.