~4h ago

Cientista de Dados Especialista (Políticas de Crédito)

BrazilRemoteFull-timemid

OtherCientista De Dados

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Quick Summary

Requirements Summary

Proven professional experience designing and managing credit risk strategies and policies through Data Science within financial institutions, fintechs, credit bureaus,

Technical Tools

OtherCientista De Dados

This is a senior, high-impact Data Science role focused on the design and evolution of credit policies across the full customer lifecycle.
You will serve as a technical reference for credit policy strategy, combining advanced analytics, experimentation, optimization, and machine learning.
The role connects Data Science with executive decision-making, translating complex data into automated and commercially effective credit strategies.
You will work closely with Risk, Fraud, Finance, Modeling, Engineering, and Product teams to balance portfolio growth with disciplined risk management.
Your work will influence approval rates, profitability, customer value, and credit risk through evidence-based policy decisions.
The environment is agile, collaborative, and highly data-driven, with significant ownership over experimentation, governance, and innovation.
This is an opportunity to shape scalable credit decision frameworks while advancing the use of AI and automation across the policy lifecycle.

Develop and maintain technical playbooks and frameworks for credit policy simulation, standardizing methodologies such as A/B testing and Champion/Challenger approaches, reusable decision-engine functions, and structured executive reporting.
Lead the design, calibration, testing, and continuous optimization of credit strategies throughout the customer lifecycle, including onboarding and origination, dynamic credit limits, pricing, account maintenance, and collections.
Apply advanced data analysis, optimization techniques, and scenario simulations, including backtesting and stress testing, to quantify the financial and risk impact of policy changes before production deployment.
Monitor active policies and strategies using business and risk indicators such as conversion rate, approval rate, NPL, overdue rates, roll rates, LTV, and risk trade-offs, recommending adjustments when performance moves outside established risk parameters.
Lead the application of decision algorithms, machine learning, mathematical optimization, and automation to evolve from rigid policies toward more dynamic and personalized credit strategies.
Integrate AI and automation throughout the policy workflow, from hypothesis generation and experimentation through production monitoring, improving operational efficiency and analytical maturity.
Act as a strategic analytical partner to Risk, Fraud, and Finance teams, translating complex data and analytical findings into clear recommendations for business and executive decision-making.
Collaborate closely with Modeling, Engineering, and Product teams to turn analytical insights and models into scalable, automated credit policies.

Requirements

~1 min read

Proven professional experience designing and managing credit risk strategies and policies through Data Science within financial institutions, fintechs, credit bureaus, or specialized consulting environments.
Advanced proficiency in programming languages used for data analysis and Data Science.
Strong knowledge of Big Data processing languages and tools for developing analytical variables and working with large-scale datasets.
Solid practical experience applying Machine Learning to credit policies, A/B testing, survival analysis, constrained optimization algorithms, and decision frameworks.
Deep understanding of credit financial metrics, including credit P&L, risk-adjusted return, loss provisions, NPL, LTV/CAC, vintage analysis, and roll rates.
Strong analytical and problem-solving capabilities, with the ability to translate complex quantitative analysis into practical business recommendations.
Ability to work effectively across technical and business teams and communicate sophisticated analytical concepts clearly to senior stakeholders.
Experience operating with autonomy in a fast-paced, collaborative, and data-driven environment.
Strong ownership mindset and attention to methodological rigor, experimentation quality, governance, and measurable business impact.