Data Scientist Fraud Monitoring

Details

Are you a pioneering data scientist who likes to think outside the box? Would you like to drive new data science solutions to tackle financial fraud? Do you want to work with cutting-edge technologies to catch criminals?

Responsibilities

• Work within Grid Channel Security together with fraud experts, data scientists, machine learning engineers and data analysts to prevent and detect payment fraud (phishing, account takeover, lost/stolen debit cards, e-commerce fraud, etc.).
• Analyse diverse data sources to produce actionable insights on fraud trends across payment channels, seasonal patterns and forecasts.
• Experiment with models and integrate new data elements to improve fraud detection capabilities.
• Collaborate with engineers to deploy models and with stakeholders to brainstorm and design data science solutions.
• Contribute to continuous improvement of fraud detection through model experimentation and operationalization.

Working environment

• Team: Fraud Monitoring team within Grid Channel Security (CISO), a multidisciplinary group of ~25 colleagues.
• Way of working: Hybrid; day-to-day work follows agile and scrum principles in an informal, collaborative atmosphere.

Requirements

• Education: Master’s degree or PhD in a quantitative field (Computer Science, Statistics, Econometrics, Engineering, Physics, Mathematics, etc.).
• Experience: 3 years of independent machine learning development experience (exploratory data analysis, model experimentation, collaborating with engineers for deployment). The job overview also lists 2+ years in brief.
• Demonstrated professional experience developing ML models in a big data environment (e.g., Spark, distributed computing frameworks).
• Strong working knowledge of Python, SQL and Spark.
• Familiarity with MLOps concepts (lineage, reproducibility, model monitoring) and experience with tools like MLflow.
• Understanding of the full data science lifecycle, including sampling, cross-validation, model evaluation metrics and feature engineering.
• Experience in a professional software development environment: use of Git, code reviews, code packaging, orchestration of jobs.
• Strong technical communication skills; able to present data science work to business and technical stakeholders and produce technical documentation.
• Project management skills and experience working in an Agile environment; ability to scope modelling projects.

Preferred team values:

• Innovative, creative problem solving and persistence.
• Intellectual curiosity and proactive collaboration with stakeholders.
• Mentoring and coaching mindset; team-oriented (no "lone wolves").
• Applicants should submit a one-page cover letter describing how their experience meets the required qualifications and scenarios demonstrating the team values.

We are offering

• Flexibility in a hybrid work environment to support strong work-life balance.
• An industry-competitive salary based on a 36 or 40-hour work week.
• A supplementary benefit budget of 11% (spendable on pre-tax fringe benefits).
• A personal development budget of EUR 1,000 per year.
• An annual public transportation pass.
• A solid pension plan.

Application process

1. Apply online
2. CV selection and feedback
3. Job interview(s)
4. Possible assessment
5. Job offer
6. Onboarding

Diversity & Inclusion

ABN AMRO emphasises equal opportunities, inclusive culture, sustainability and internal career opportunities. The bank conducts annual reviews to ensure equal pay for equal work.