The Data Science team, focused on Risk Management, our work is centered around compliance and due diligence, specifically Fair Lending and Anti Money Laundering by unmasking the bad guys and solving problems. Our team is working with vital analytics to ensure we are within the guidelines for banking regulations, managing the bank's exposure to loss and risk. Our team uses SAS, Python and various machine learning models to provide on-demand analysis.
You will
Design, develop, and implement predictive models, machine learning algorithms, and statistical techniques to assess fair lending risks and detect potential instances of discrimination
Analyze large datasets to identify patterns, trends, and potential areas of concern related to Fair Lending practices
Utilize advanced statistical methods to evaluate model performance, including model calibration, validation, and interpretation of results
Collaborate with cross-functional teams to understand business requirements and develop data-driven solutions for Fair Lending compliance
Be committed to diversity, equity, and inclusion, with a passion for promoting fairness and equality in lending practices
Requirements:
Proficient in Hebrew and English both written and verbal, sufficient for achieving consensus and success in a remote and largely asynchronous work environment – Must
Bachelor's degree in Statistics, Mathematics, Data Science, Economics, or a highly quantitative field – Must (Advanced degree an advantage)
3+ years of experience in data analysis, statistical modeling, and predictive analytics within the financial services industry, preferably in Fair Lending or credit decisioning
Strong technical proficiency in data science tools and programming languages such as Python, R, SQL, with experience in developing predictive models and machine learning algorithms.
Strong knowledge of advanced analytics and machine learning techniques such as Regression and Classification algorithms, including linear and logistic regression, random forest and gradient boosting, k-nearest neighbors, support vector machines (SVMs) and other techniques
Experience working with cloud-based data platforms and technologies such as AWS and Sagemaker for scalable data analytics and machine learning
Experience working with large-scale datasets
Ability to communicate findings and recommendations to stakeholders
Strong problem-solving skills while paying attention to detail
Ability to work independently and collaboratively in a fast-paced environment, managing multiple projects and priorities simultaneously
Proficient in Hebrew and English both written and verbal, sufficient for achieving consensus and success in a remote and largely asynchronous work environment – Must
Bachelor's degree in Statistics, Mathematics, Data Science, Economics, or a highly quantitative field – Must (Advanced degree an advantage)
3+ years of experience in data analysis, statistical modeling, and predictive analytics within the financial services industry, preferably in Fair Lending or credit decisioning
Strong technical proficiency in data science tools and programming languages such as Python, R, SQL, with experience in developing predictive models and machine learning algorithms.
Strong knowledge of advanced analytics and machine learning techniques such as Regression and Classification algorithms, including linear and logistic regression, random forest and gradient boosting, k-nearest neighbors, support vector machines (SVMs) and other techniques
Experience working with cloud-based data platforms and technologies such as AWS and Sagemaker for scalable data analytics and machine learning
Experience working with large-scale datasets
Ability to communicate findings and recommendations to stakeholders
Strong problem-solving skills while paying attention to detail
Ability to work independently and collaboratively in a fast-paced environment, managing multiple projects and priorities simultaneously
This position is open to all candidates.
