About The Job Data Scientist – Transaction Monitoring-260617-OF-005
Location: Utrecht
Duration: ASAP – June 2027
Hours: 36 hours per week
Language: English (Mandatory)
Role Overview
We are seeking a Data Scientist with a passion for combating financial crime through data analytics. In this role, you will develop and optimize transaction monitoring models used to detect suspicious activity and identify patterns related to money laundering and terrorist financing. You will work with large-scale datasets and collaborate with business, analytics, and technology teams to improve detection capabilities and support data-driven decision-making.
Key Responsibilities
- Develop and enhance transaction monitoring detection rules through data analysis, model development, and robust coding practices.
- Analyze customer and transaction data to identify suspicious patterns and emerging risks.
- Identify and evaluate new data sources to improve model effectiveness.
- Support the implementation, maintenance, and continuous improvement of analytical models in production environments.
- Collaborate with data scientists, analysts, IT specialists, and business stakeholders to ensure consistent methodologies and best practices.
- Communicate analytical findings and model insights clearly to both technical and non-technical stakeholders.
Required Qualifications
- Master's degree in Data Science, Econometrics, Mathematics, Statistics, Computer Science, or a related quantitative field.
- 3–5 years of experience in Data Science or Data Analysis, preferably working with large datasets.
- Strong programming skills in Python and PySpark.
- Experience with Azure Databricks is advantageous.
- Experience implementing and maintaining analytical models in production environments is preferred.
- Strong statistical and analytical skills.
- Excellent verbal and written communication skills in English.
- Ability to explain complex analytical concepts to business stakeholders.
Preferred Experience & Skills
- Experience in transaction monitoring, financial crime detection, AML, fraud analytics, or related domains.
- Knowledge of machine learning techniques and model development.
- Experience working in Agile environments.
- Strong problem-solving skills and a structured, detail-oriented approach.
- Ability to work effectively in multidisciplinary teams.
Personal Attributes
- Motivated to contribute to the fight against financial crime.
- Analytical, proactive, and results-oriented.
- Strong collaboration and stakeholder management skills.
- Comfortable working in a dynamic, high-impact environment.
