Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Agile @ 4
Communication @ 7
Data Analysis @ 4
Data Science @ 4
E-commerce
Experimentation @ 4
Git @ 4
MLFlow @ 3
MLOps @ 3
Machine Learning @ 4
Mathematics @ 6
Project Management @ 6
Python @ 7
SQL @ 7
Security
Software Development @ 4
Spark @ 4
Statistics @ 6
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Are you a pioneering data scientist interested in developing data science solutions to combat financial fraud? In the Grid Channel Security team, you will work with fraud experts, data scientists, machine learning engineers, and data analysts to protect customers from phishing, account takeover, lost or stolen debit cards, e-commerce fraud, and other threats.
Responsibilities
- Analyse diverse data sources to generate actionable insights.
- Identify fraud trends across payment channels.
- Evaluate data fields that could improve fraud detection.
- Experiment with different modelling approaches and enhancements.
- Recognise seasonal patterns and create reliable forecasts.
- Contribute to continuous improvements in fraud detection through model experimentation and the integration of new data elements.
- Collaborate with colleagues to develop data science solutions that help prevent fraud.
- Work according to Agile and Scrum principles in a multidisciplinary Fraud Monitoring team.
Requirements
- A master’s degree or PhD in a quantitative field such as Computer Science, Statistics, Econometrics, Engineering, Physics, or Mathematics.
- Five years of independent machine learning development experience, including exploratory data analysis, model experimentation, and collaboration with engineers on model deployment in real-world applications.
- Professional experience developing machine learning models in a big data environment, such as Spark or other distributed computing frameworks.
- Strong working knowledge of Python, SQL, and Spark.
- Familiarity with MLOps concepts including lineage, reproducibility, and model monitoring, plus experience with tools such as MLFlow.
- Understanding of the full data science lifecycle, including sampling, cross-validation, model evaluation metrics, feature engineering, and trade-off decisions.
- Experience in a professional software development environment, including Git, code reviews, code packaging, and job orchestration.
- Strong technical communication skills and the ability to present data science work to business and technical stakeholders.
- Technical documentation skills.
- Project management skills, including the ability to scope modelling projects.
- Experience working in an Agile environment.
- A collaborative, innovative, persistent, proactive, and intellectually curious mindset.
- A one-page cover letter describing how your experience meets the required qualifications and demonstrating the team’s values.
Working Environment
Grid Channel Security within the Corporate Information Security Office is responsible for preventing and detecting payment fraud and determining fraud policy and strategy. The Fraud Monitoring team is a multidisciplinary group of approximately 25 colleagues focused on protecting customers. The team works in a hybrid way and follows Agile and Scrum principles.
Benefits
- Gross monthly salary of €5,304 to €7,577 based on a 36-hour work week, including holiday allowance and a flexible benefits budget.
- Excellent pension scheme.
- Hybrid working, with the possibility of working from home in consultation with the team and an ergonomic home-office setup.
- Five weeks of vacation per year, two mandatory additional days off, and the option to purchase up to four extra weeks of vacation annually.
- Five additional days off for personal development or volunteer work.
- Annual development budget of €1,000, which can accumulate up to €3,000.
- Annual public transport pass providing free public transportation throughout the Netherlands for business and private use.
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