Data Scientist
Role details
Job location
Tech stack
Job description
Do you want to work on intelligent models that directly impact the automatic loan approval process? Within Consumer Lending, you will join the RICE squad. Together with engineers and a product owner, you will focus on improving and extending our transaction classification models. Your role Your main responsibility is to optimize and further develop models that classify transactions. These models play a crucial role in the automatic loan approval process. You will also monitor model performance and collaborate closely with engineers, analysts, and business stakeholders to deliver the best possible customer experience. We work with weekly Kanban sessions to align tasks and priorities.
Requirements
Do you have experience in Spark?, Do you have a Master's degree?, * A Master's degree in Data Science, Computer Science, Econometrics, or a related field
- Experience with Python, pandas or polars, Spark, OOP, SQL, test driven development, model development, model monitoring, and software engineering best practices
- Strong analytical skills and attention to detail
- Excellent communication skills and the ability to clearly explain complex analyses
- Strong stakeholder management skills
- Proficiency in Dutch
Nice to have
- Experience with CI/CD, deployments, and Docker
- Experience with GCP
- Strong engineering skills
- Familiarity with transactional and customer level data
- Experience in the Consumer Lending domain or knowledge of credit processes
Tech stack
- Languages and data: Python, SQL
- Development environment: VSCode, terminal
- Versioning and CI/CD: Azure DevOps (Repos, Pipelines)
- Containerization: Docker
- Data and observability: Elastic, Grafana
- ML platform: MLflow, DAP/MLP (internal platforms)
- Orchestration: Airflow
- APIs: Consuming REST APIs
Benefits & conditions
What we offer
- An initial period of 4 weeks at 36 hours per week, followed by 24 hours per week until the end of October 2026
- A salary range of €5,485.70 to €6,510.00 based on 36 hours per week and depending on experience. For 24 hours per week, the salary is calculated pro rata
- Holiday allowance, 13th month payment and Pension scheme
- Hybrid working, home office and travel expense reimbursement
If you are eager to apply your expertise in data science to models that directly influence customer outcomes in Consumer Lending, we look forward to receiving your application.
