Credit Data Science Specialist
New
J
JobgetherFinance
Work from anywhere within Brazil.Full-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLData AnalysisMachine LearningData visualizationGenerative AI
Requirements
- Bachelor’s degree in Statistics, Engineering, Economics, Mathematics, Computer Science, or a related quantitative discipline.
- Hands-on experience using Python to develop and apply statistical and machine learning models.
- Solid understanding of the credit lifecycle and experience working with credit and collections indicators such as FPD, PDD, Vintages, Roll Rates, SCR, and Serasa data.
- Proven experience developing predictive models and performing complex statistical calculations.
- Experience creating propensity-to-pay models and analyzing collection and recovery strategies.
- Advanced SQL skills, with the ability to extract, transform, and analyze large-scale transactional datasets.
- Strong understanding of statistical modeling, data analysis, and machine learning concepts.
- Experience with data visualization, dashboards, and KPI development.
- Familiarity with AI tools.
- Strong communication and collaboration skills, particularly when working with data engineering and business stakeholders.
Responsibilities
- Develop, implement, and monitor credit scoring and churn predictive models using transactional and financial data.
- Analyze revenue trends using statistical techniques such as linear regression and confidence intervals.
- Apply generative AI and advanced analytical approaches to automate and scale credit analysis.
- Conduct portfolio analysis and backtesting, including Vintages and Roll Rates, to inform credit limits, pricing, and retention strategies.
- Build propensity-to-pay models to improve debt recovery and support segmentation of external collection strategies.
- Evaluate the financial feasibility of judicial versus extrajudicial collection approaches.
- Design and maintain real-time dashboards and operational reporting covering KPIs such as FPD, PDD, and debt renegotiation effectiveness.
- Partner with data engineers to develop a useful data lake and transform raw data into structured variable books.
- Work with large-scale transactional datasets to generate reliable business insights.
- Collaborate with cross-functional teams to translate analytical findings into credit and collections policies.
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