Data Scientist Specialist – Lending
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Job Description
Specialist Data Scientist leading development of credit models and decision strategies at RecargaPay. Focused on risk management and machine learning solutions in a fully remote setup.
Responsibilities:
- Develop and implement real-time scoring models to quantify the risk level of transactions and credit operations.
- Build predictive models using internal and third-party data to optimize user onboarding and reduce losses.
- Evolve static rule engines into dynamic, graph-based systems, enabling smarter and more adaptable rule management.
- Lead the adoption of new technologies such as Databricks and Data Catalog, advocating best practices and facilitating the transition to a more modern and efficient data environment.
- Analyze large volumes of transactional, user-behavior, and demographic data to identify patterns, trends, and opportunities to improve risk assessment.
- Develop and implement geolocation and fingerprinting solutions to enhance risk evaluation.
- Guide and mentor team members by sharing expertise and knowledge, and lead key projects from a technical perspective.
- Monitor and analyze the performance of credit models, focusing on their stability and accuracy.
Requirements:
- Programming and Tools: Proficiency in Python, SQL, and Spark. Experience with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn. Familiarity with platforms like Databricks, AWS, and Azure. Experience with Git for version control and collaboration.
- Analytical Modeling & Machine Learning: Proven experience building and deploying machine learning models. Deep knowledge of classification, regression, and clustering algorithms, as well as feature engineering and model selection techniques. Experience with model explanation techniques such as SHAP, bivariate analysis, and weight of evidence.
- Data Analysis: Ability to work with large datasets and write efficient, optimized SQL queries. Experience with exploratory data analysis and feature evaluation.
- Statistical Knowledge: Strong understanding of A/B testing and statistics, including experimental design and statistical significance. Basic knowledge of predictive modeling metrics such as AUC, KS, precision, and recall.
- Other Skills: Knowledge of data modeling principles and experience building robust, scalable data models. An analytical mindset with a strong focus on problem-solving. Strong mathematical skills with the ability to optimize complex problems.
- Problem-Solving Orientation: Ability to research existing solutions from other contexts and adapt them to the problem you are working on.
- Innovative Thinking: Apply theoretical knowledge of statistics, economics, and behavioral finance to optimize proposed solutions.
- Communication: Ability to translate complex technical findings into actionable business insights and communicate them clearly to both technical and non-technical audiences.
- Collaboration: Willingness to work in a collaborative and dynamic environment.
- Bonus Points
- Proficiency in PySpark for distributed data processing in large-scale environments.
- Experience with MLOps in machine learning projects.
- Familiarity with model monitoring in production environments.
- Exposure to Open Finance, credit bureau data, or behavioral features.
- Previous experience in fintechs or financial services companies.
Benefits:
- Remote — wherever you are, you are part of the team!
- Home office support via a monthly deposit through the RecargaPay app.
- Health and dental plans with no copayment.
- Life insurance.
- Flexible meal allowance (via Flash).
- TotalPass membership for wellness and fitness.
- Spanish or Portuguese lessons.
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