Sr. Data Science Analyst

Proven track record of independently developing and deploying machine learning models in a production environment. 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. Strong experience in building and implementing machine learning models. Solid knowledge of classification, regression, and clustering algorithms. Experience with model explanation techniques. Ability to handle large datasets and write efficient SQL queries. Strong experience with exploratory data analysis. Strong understanding of A/B testing and statistics. Proficient knowledge of predictive modeling metrics like AUC, KS, precision, and recall. Knowledge of data modeling principles. An analytical mindset with a strong focus on problem-solving. Strong mathematical skills. Applying theoretical knowledge of statistics, economics, and behavioral finance. Ability to translate complex technical findings into actionable business insights. Willingness to work in a collaborative and dynamic environment.

Develop and implement advanced machine learning models to quantify risk and optimize user onboarding. Design and conduct complex data analyses on large datasets to identify patterns and trends. Optimize and maintain existing credit models for stability, accuracy, and business impact. Lead the design and execution of A/B tests for credit decision strategies. Build and optimize robust and scalable data pipelines for model training and inference. Contribute to best practices in data science, including documentation and version control.