Data Scientist

Job Description: We are seeking a highly skilled Data Scientist to join our Anti Money Laundering (AML) team within the payments industry. The role involves building and deploying advanced Machine Learning (ML) models to detect and prevent financial crimes, optimize fraud detection processes, and ensure compliance with regulatory requirements. You will work closely with cross-functional teams including compliance, risk, data engineering, and product, with a direct impact on safeguarding financial ecosystems.

 Responsibilities: 

 • Design, develop, and deploy machine learning models for AML detection, fraud prevention, and suspicious transaction monitoring.

 • Apply statistical techniques and advanced analytics to detect anomalies and patterns in large-scale transactional datasets. 

 • Conduct exploratory data analysis (EDA) to identify behavioral trends, money laundering typologies, and high-risk activities. 

 • Develop scalable data pipelines using SQL, Python, and AWS for model training, validation, and monitoring. 

 • Work with Feedzai and other AML platforms to integrate ML solutions into production environments. 

 • Continuously monitor model performance, retrain, and improve detection capabilities to adapt to evolving threats. 

 • Provide mentorship and guidance to junior data scientists and analysts. 

 • Document methodologies and ensure solutions meet industry regulations and audit standards. Classification |

 INTERNAL Skills: 

 • Masters or Ph.D. in Computer Science, Statistics, Data Science, Applied Mathematics, or related field.

 • 5+ years of data science experience with at least 4+ years in FinTech / Payments / Banking / AML. 

 • Strong hands-on experience in Python, SQL, Statistics, and Machine Learning techniques (classification, anomaly detection, clustering, NLP). • Big Data tools and cloud environments, preferably AWS (S3, SageMaker, etc.). 

 • AML-focused platforms, with Feedzai experience strongly preferred.

 • Knowledge of money laundering typologies, regulatory guidelines.

 • Proven track record of building ML models for financial fraud detection or AML systems in a production environment. 

 • Excellent problem-solving, analytical thinking, and communication skills.