Data scientist

Design and implement robust data pipelines to collect, clean and prepare relevant consumer financial data.
Conduct exploratory data analysis to identify key features and patterns associated with financial distress.
Develop and evaluate various machine learning algorithms
Improving model performance through feature engineering
Interpret model results and translate complex statistical concepts into actionable insights
Monitor and improve model performance over time.
Prepare clear and concise documentation for the model development process.
Stay up to date on the latest developments in consumer credit modeling and machine learning.
Required profile of candidates

Bachelor's degree in data science, statistics, computer science, or related field.
Proven experience developing and deploying machine learning models, preferably in a financial context.
Strong understanding of statistical modeling techniques (e.g. logistic regression, decision trees, random forests).
Mastering machine learning libraries
Experience with data organization tools and techniques (e.g. SQL, Python libraries).
Excellent analytical and problem-solving skills.