Data Scientist Specialist - Lending
New
United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonSQLGitMachine LearningNumpyAzurePandasSparkA/B testingDatabricksscikit-learn
Requirements
- Strong proficiency in Python, SQL, and Spark
- Experience using libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib
- Solid experience building and deploying machine learning models (classification, regression, clustering, feature engineering)
- Deep understanding of model evaluation techniques (AUC, KS, precision, recall, statistical significance testing)
- Strong background in A/B testing, experimental design, and statistical analysis
- Experience working with large-scale datasets and writing optimized SQL queries
- Knowledge of model interpretability techniques (SHAP, weight of evidence, bivariate analysis)
- Familiarity with cloud and data platforms such as Databricks, AWS, or Azure
- Familiarity with version control tools like Git
- Strong problem-solving mindset
- Excellent communication and collaboration skills
Responsibilities
- Develop and deploy real-time scoring models to assess credit and transaction risk with high accuracy and scalability.
- Build predictive machine learning models using internal and external data to optimize onboarding and reduce credit losses.
- Evolve static rule-based systems into dynamic, graph-based decision engines for more adaptive risk management.
- Analyze large-scale behavioral, transactional, and demographic datasets to identify patterns and improve risk strategies.
- Design and implement advanced solutions such as fingerprinting and geolocation-based tracking to enhance fraud and risk detection.
- Monitor, evaluate, and improve the performance, stability, and accuracy of credit and risk models in production environments.
- Lead technical initiatives, mentor team members, and contribute to best practices in data science and machine learning engineering.
- Support the adoption of modern data platforms and tools, including Databricks and data catalog systems, to improve scalability and efficiency.
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