Job Title

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 ResponsibilitiesDesign, 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.Required Skills5–8 years of overall experience in Data Science, Machine Learning, or related roles.3–5 years of hands-on experience in Python-based data science and machine learning.Strong proficiency in Python and data science libraries such as Pandas, NumPy, and Scikit-learn.1–2 years of experience working with Gradient Boosting frameworks such as XGBoost, LightGBM, or CatBoost.Strong knowledge of SQL and PostgreSQL for data extraction and analysis.Hands-on experience with MLflow for experiment tracking and model versioning.Experience with Jupyter Notebook or JupyterHub for model experimentation and data exploration.Proficiency in Git and Bitbucket for version control and collaborative development.Familiarity with Linux/Unix environments and basic Shell scripting.Understanding of machine learning techniques including classification, anomaly detection, and feature engineering.Knowledge of data visualization tools such as Plotly, Matplotlib, or Seaborn.Strong analytical thinking, problem-solving ability, and attention to detail.Good communication and collaboration skills.Added AdvantageExperience working with large datasets or big data technologies such as Spark or Dask.Prior experience in Fintech, Banking, or Cybersecurity domains.Understanding of MLOps concepts including model deployment and monitoring in production environments.Familiarity with package management tools such as Conda, Pip, or virtual environments.Knowledge of data privacy and security best practices when handling production data.