Senior Data Scientist – Credit Risk, Modeling

Banco Fibra

. Develop, implement, and enhance statistical and Machine Learning models applied to credit risk.

Posted 11/7/2026full-timeSão Paulo • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and implementing statistical and Machine Learning models for credit risk, with a strong focus on model validation and performance monitoring. Proficient in translating complex analyses into actionable insights to support strategic decision-making.

Highest-signal resume keywords

Machine Learning TechniquesCredit Risk ModelingPython ProficiencySQL ProficiencyModel Validation

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Statistical AnalysisPortfolio SegmentationRegression ModelsClassification ModelsPerformance MonitoringRisk AnalysisAlgorithm DevelopmentData AnalysisBacktesting TechniquesLarge Dataset Familiarity

Soft Skills

Analytical SkillsCommunication Skills

Tools & Technologies

DatabricksPower BI

Industry Keywords

Credit RiskPDLGDExpected Loss ModelingCMN Resolution No. 4,966

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Develop, implement, and enhance statistical and Machine Learning models applied to credit risk.
Build scalable analytical solutions to estimate PD, LGD, and expected loss parameters, as well as analyze portfolio behavior.
Implement validation routines, performance monitoring, and model stability tracking.
Conduct analyses and tests of new credit strategies, assessing their impact, risk, and return.
Create algorithms and tools to monitor risk indicators and identify trends, deviations, and signs of portfolio deterioration.
Work closely with Data, Technology, and Business teams to build and deploy solutions.
Translate technical results into clear analyses and recommendations to support decision-making.
Contribute to the evolution of the team’s analytical architecture, methodologies, and tools.

Requirements

What you’ll need

Bachelor’s degree in Statistics, Mathematics, Economics, Engineering, Business Administration, Data Science, Computer Science, or a related field.
Knowledge of credit risk and PD, LGD, and expected loss modeling.
Knowledge of CMN Resolution No. 4,966.
Experience with credit risk models, portfolio segmentation, and customer behavior analysis.
Proficiency in Python and SQL, plus hands-on experience with Databricks and Power BI.
Experience with Machine Learning techniques, classification and regression models, and statistical methods.
Knowledge of model validation, backtesting, and monitoring techniques.
Strong analytical skills and familiarity with large datasets.

Benefits

Comp & perks

Compensation aligned with the financial services market
Meal and food allowance
13th-month food allowance payment
Profit Sharing (PLR)
Medical insurance
Transportation allowance
Gympass
Dental insurance
Daycare, nanny, and children with disabilities allowance
Extended maternity and paternity leave
Annual flu vaccination
Birthday day off
Flexible working hours
Educational partnerships