Not specified; 13th month salary; 8% holiday pyyme...
Hybrid
Strong background in statistics
Experience building classifiers with boosting algorithms
Fluent coding in python, pyspark, and sql
This hybrid role involves optimizing product funnels, developing complex reports, and designing A/B tests while also building full data science pipelines using tree-based approaches
Job Summary
This hybrid role involves optimizing product funnels, developing complex reports, and designing A/B tests while also building full data science pipelines using tree-based approaches.
The team is part of ING's Retail Banking Analytics Chapters, offering a world-class working environment with highly skilled colleagues dedicated to knowledge sharing.
Candidates will collaborate with cross-functional teams including product managers and engineers to derive actionable insights from credit risk and customer interaction data.
Matching Summary
This hybrid role involves optimizing product funnels, developing complex reports, and designing A/B tests while also building full data science pipelines using tree-based approaches.
Salary
Not specified; 13th month salary; 8% Holiday payment
Skills & Requirements
Must-have
Strong background in statistics
Experience building classifiers with boosting algorithms
Fluent coding in Python, Pyspark, and SQL
At least 4 years experience in financial sector
Domain knowledge in credit risk or collections
Nice-to-have
Excellent communication and presentation skills
Curious mindset with continuous learning
Collaborative team player
Interest in marketing intelligence
Key Requirements
Master's degree in econometrics or strong statistics background
Minimum 4 years of financial sector experience
Proficiency in Python, Pyspark, and SQL
Expertise in boosting algorithms for classification