Data Scientist Specialist (Credit Policies)
New
J
JobgetherCredit risk
Remote work model in Brazil.Full-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- Machine LearningData scienceA/B testing
Requirements
- Have professional experience designing and managing credit risk strategies and policies through Data Science in financial institutions, fintechs, credit bureaus, or specialized consulting environments.
- Demonstrate advanced proficiency in programming languages used for data analysis and Data Science.
- Have strong knowledge of Big Data processing languages and tools for developing analytical variables and working with large-scale datasets.
- Bring practical experience applying Machine Learning to credit policies, A/B testing, survival analysis, constrained optimization algorithms, and decision frameworks.
- Understand credit financial metrics, including credit P&L, risk-adjusted return, loss provisions, NPL, LTV/CAC, vintage analysis, and roll rates.
- Translate complex quantitative analysis into practical business recommendations.
- Work effectively across technical and business teams and communicate analytical concepts to senior stakeholders.
- Operate autonomously in a fast-paced, collaborative, data-driven environment.
- Demonstrate ownership and attention to methodological rigor, experimentation quality, governance, and measurable business impact.
Responsibilities
- Develop technical playbooks and frameworks for credit policy simulation, A/B testing, Champion/Challenger approaches, reusable decision-engine functions, and executive reporting.
- Lead the design, calibration, testing, and optimization of credit strategies across onboarding, origination, credit limits, pricing, account maintenance, and collections.
- Apply data analysis, optimization, backtesting, and stress testing to assess the financial and risk impact of policy changes before deployment.
- Monitor active policies using business and risk indicators, and recommend adjustments when performance moves outside established risk parameters.
- Apply decision algorithms, machine learning, mathematical optimization, and automation to develop dynamic and personalized credit strategies.
- Integrate AI and automation across policy workflows, from hypothesis generation and experimentation to production monitoring.
- Partner with Risk, Fraud, and Finance teams to translate analytical findings into business and executive recommendations.
- Collaborate with Modeling, Engineering, and Product teams to turn insights and models into scalable, automated credit policies.
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