Data Science Lead Analyst (VP) – Data Science

Key Skills

Python Programming, SQL, Big Data Technology, Data science techniques, Leadership Skill, Writing, Data Analytics, R Programming, SAS, Effective communication skills

Job Description

The Data Science Lead Analyst will be required to conduct statistical analyses related to credit cards, small business loans and retail bank consumer lending products, focusing on fair lending aspect in order to identify and measure Citi’s exposure to fair lending risk. The position requires strong quantitative background, compliance risk management attitude, critical thinking skills, and experience in data validation.

Responsibilities:

• Design and employ statistical analysis and modeling techniques to assist Fair Lending Analytics and all relevant business units (fair lending officers and fair lending committees) in their compliance with Citigroup Fair Lending Policy and all Fair Lending laws and regulations.
• Enhance and/or validate existing in-house fair lending analytical programs and practices used to identify fair lending risks through the entire credit lifecycle, including marketing, underwriting, pricing, existing customer management and loss mitigation; or fair lending risks in score cards and other analytical models used in Citi's consumer businesses.
• Document methodology and develop procedures to standardize further approach, and develop reporting templates to resolve any questions related to analytical methodology or data integrity.
• Follow implemented in-house processes for data mining, data assessment, validation, analysis, and correction to determine any limitations on reliability or usability.
• Periodically re-validate processes to ensure validity, efficiency, and accuracy; and to identify enhancement opportunities.
• Prepare and present statistical results and data sets for analysis in support of regulatory requests and examinations.
• Work with examiners and business units to resolve any questions related to analytical methodology or data integrity.
• Advise the business units, regulatory examiners, and other interested parties on quantitative methods and analysis, and present results in the form of articles, reports, or presentations.
• Develop and deepen Subject Matter Expertise in large Fair Lending, and CRA as well as in specific fair lending analytics field through internal, external and on-the-job training.
• Special projects assigned at the direction of the direct manager or Head of Fair Lending Analytics ICRM.
• Additional duties as needed

Qualifications:

• Direct experience in fair lending modeling and analytics strongly preferred
• Expertise and hands-on experience in advance programming using: SAS / R / Python, and SQL for basic data mining; additional experience and knowledge of Big Data tools preferred
• In-depth knowledge and understanding, and demonstrated experience working with logistic regression model development, univariate and multivariable statistics, sampling designs, disparities analysis
• Good skills in oral and written communication in English
• Must be a team player and able to support team deliverables while delivering on individual responsibilities.
• Ability to work under pressure and manage multiple concurrent responsibilities and prioritize according to shifting and sometimes accelerated deadlines
• Strong ability to work both collaboratively and independently
• Knowledge of mortgage and housing lending products and regulatory requirements including ECOA and FHA preferred
• Fair Lending knowledge and experience highly preferred
• Credit cards risk management experience is a plus

Education:

• Bachelor’s degree or 10+ years equivalent experience in a highly quantitative field, such as data science, applied economics, applied statistics, mathematics, or physics
• 8+ years experience in a highly quantitative field, such as data science, applied economics, applied statistics, mathematics, or physics and Master’s degree preferred.
• 3+ years experience in a highly quantitative field, such as data science, applied economics, applied statistics, mathematics, or physics and Ph.D. preferred.