Credit Data Science Specialist

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We believe work should be fulfilling, inspiring, and balanced. Here, you’ll find a team that values wellbeing, collaboration, and different perspectives, where passion and creativity push boundaries to create real impact. Your contributions will help shape a healthier, more balanced world for you and millions of people globally. 

We are hiring a Credit Data Science Specialist in our Financial Solutions (New Initiatives) area in Brazil!

This is a Remote – Brazil position, meaning you can work from anywhere within the country. Please note that this role is only open to candidates in Brazil. 

We are looking for a hands-on person with solid credit knowledge to join the Credit and Collections team. More than a purely technical profile, we need someone curious, who wants to understand how the business works and support it with data, information, and insights.
 
The Data Scientist will work in partnership with data engineers to build a data lake that is useful for modeling and for generating credit and collections policies. This person will be able to move from raw data to proposing rich variable books and features for the business - turning information into strategic advantage.
 
Among their main responsibilities, this person will create, implement, and monitor predictive models, as well as develop a cockpit of operational and managerial indicators to track the health and performance of the Credit and Collections Operation.

• Risk & Churn Predictive Modeling: Develop and monitor Credit Scoring and Churn models by leveraging transactional data from the Wellhub platform, including the calculation of revenue trends using linear regression and 99% confidence intervals.
• AI-Driven Credit Automation: Implement Generative AI and advanced algorithms to automate the credit analysis process, ensuring scalability while maintaining strict compliance with eligibility criteria and internal validation rules.
• Portfolio Strategy & Backtesting: Conduct quantitative studies and backtesting on portfolio behavior (Vintages, Roll Rates) to optimize credit limits, interest rate pricing, and proactive retention strategies.
• Collection Efficiency & Recovery: Analyze debt recovery performance and build propensity-to-pay models to segment the external collection strategy, including the financial feasibility analysis for judicial actions.
• Financial Performance Reporting: Design real-time dashboards to track critical KPIs such as First Payment Default (FPD), Provision for Doubtful Accounts (PDD), and the effectiveness of debt renegotiations. 
• Live the mission: inspire and empower others by genuinely caring for your own wellbeing and your colleagues. Bring wellbeing to the forefront of work, and create a supportive environment where everyone feels comfortable taking care of themselves, taking time off, and finding work-life wellness.

• Quantitative Education: Bachelor’s degree in Statistics, Engineering, Economics, Mathematics, or Computer Science.
• Data Science & AI Proficiency: Experience in Python, applying regression and classification algorithms to financial forecasting, combined with an openness to working with a variety of AI tools to drive efficiency gains in data analysis and KPI generation.
• Credit Risk Environment: Knowledge of credit lifecycles, including FPD (First Payment Default), Vintages, and SCR/Serasa data analysis.
• Statistical Modeling: Proven ability to manage complex calculations, in addition to predictive modeling using statistical and machine learning concepts.
• Collection & Recovery Strategy: Experience building propensity-to-pay models and conducting financial feasibility analysis for judicial versus extrajudicial collection.
• Advanced Data Manipulation: Mastery of SQL for extracting insights from large-scale transactional datasets and managing internal/external validation databases.
• Business English: Not mandatory, but considered a strong differential - proficiency in English supports collaboration with peer teams in other countries and helps navigate the global strategy of "Be Global, Be Fit, and Be Loved."

We recognize that individuals approach job applications differently. We strongly encourage all aspiring applicants to go for it, even if they don't match the job description 100%. We welcome your application and will be delighted to explore if you could be a great fit for our team. For this specific role, please note that prior experience in regression and classification algorithms for financial forecasting and data visualization, have a strong understanding of credit lifecycle metrics, including FPD (First Payment Default), Vintages, and SCR/Serasa, as well as experience managing complex calculations and developing predictive models using statistical and machine learning techniques are mandatory requirements.

We aim to create a collaborative, supportive, and inclusive space where everyone knows they belong. 

Wellhub is committed to creating a diverse work environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, gender identity or expression, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law.

Our commitment to inclusion also extends to how we recognize and reward our people. We’re proud to be Syndio Fair Pay Certified, reflecting our ongoing dedication to equitable and fair pay practices across our global team. Read more about it here.

Questions on how we treat your personal data? See our Aviso de Privacidade para Candidatos. 

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