Data Scientist Specialist - Lending
New
R
RecargaPayFintech
BrazilFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonSQLGitMachine LearningNumpyAzurePandasSparkData modelingA/B testingDatabricksscikit-learn
Requirements
- Proficiency in Python, SQL, and Spark.
- Experience with Pandas, NumPy, Matplotlib, and Scikit-learn.
- Familiarity with Databricks, AWS, and Azure.
- Experience with Git for version control and collaboration.
- Demonstrated experience in building and implementing machine learning models.
- Deep knowledge of classification, regression, and clustering algorithms.
- Experience with feature engineering and model selection techniques.
- Experience with model explanation techniques like SHAP, bivariate analysis, and weight of evidence.
- Ability to handle large datasets and write efficient, optimized SQL queries.
- Experience with exploratory data analysis and evaluating model features.
- Strong understanding of A/B testing and statistics, including experimental design and statistical significance.
- Basic knowledge of predictive modeling metrics like AUC, KS, precision, and recall.
- Knowledge of data modeling principles and experience building robust and scalable data models.
- An analytical mindset with a strong focus on problem-solving and strong mathematical skills.
- Ability to research existing solutions and adapt them to specific problems.
- Innovative thinking applying statistics, economics, and behavioral finance.
- Ability to translate complex technical findings into actionable business insights and communicate them clearly.
- Willingness to work in a collaborative and dynamic environment.
Responsibilities
- Support the development, monitoring, and evolution of credit models and decision strategies.
- Lead the development and implementation of advanced machine learning models and analytical solutions to solve complex credit and transaction risk challenges.
- 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 ones, enabling more intelligent and adaptable rule management.
- Lead the adoption of new technologies like Databricks and Data Catalogue, advocating for best practices and facilitating a 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 for improvement in risk assessment.
- Develop and implement fingerprinting and geographic tracking solutions to improve risk assessment.
- Guide and mentor team members, sharing experience and knowledge, and leading key projects from a technical perspective.
- Monitor and analyze the performance of credit models, focusing on their stability and accuracy.
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