Directors, Data Science - job 4 of 16

As a Director, Data Science at American Express, you will be responsible for driving strategic analysis of credit metrics, leading the analytical support for credit provision, and presenting key findings to management. You'll be integral in developing analytics that identify trends and optimize business and risk decisions, making this role crucial in shaping our credit strategies.

To qualify for the Directors, Data Science position at American Express, candidates must possess a Master’s degree in Finance, Statistics, or a related field. Additionally, at least two years of experience in risk management is a must, along with proficiency in credit risk data analysis, machine learning models, and relevant software such as Base SAS and SQL.

Yes, American Express offers the flexibility of telecommuting for up to two days per week for the Directors, Data Science position. This flexibility helps maintain a healthy work-life balance while allowing you to contribute effectively to your team and the organization.

Directors, Data Science at American Express will engage in various projects focused on analyzing credit metrics, optimizing risk decisions, and implementing data-driven strategies. You will work with advanced machine learning techniques to enhance our credit risk capabilities and improve overall business outcomes.

American Express is committed to supporting the well-being of its employees and offers a comprehensive benefits package for Directors, Data Science. This includes competitive salaries, performance bonuses, retirement savings plans, healthcare options, paid parental leave, and ongoing career development opportunities.

Highlight your previous roles where you've analyzed credit risk data, focusing on tools and techniques used, your approach to identifying trends, and any successful projects that resulted in optimized financial strategies.

Discuss the data science tools you have used, such as Base SAS, SQL, and any libraries in R for machine learning. Emphasize how you've applied these tools in real-world situations to derive insights or solve problems.

Explain your systematic approach to troubleshooting. Provide an example of a challenging data issue you faced and the steps you took to resolve it, highlighting your critical thinking and problem-solving skills.

Share your approach to simplifying complex data insights into actionable recommendations for stakeholders, focusing on how you tailor your communication style based on the audience’s technical background.

Mention specific resources you rely on, such as industry blogs, workshops, and online courses. Also, discuss your participation in data science communities and any recent learnings that have influenced your work.

Provide an example of a collaborative project, your role in leading the team, the challenges faced, and the outcomes. Highlight your leadership skills and your ability to align team efforts with organizational goals.

Discuss your understanding of relevant regulations, experiences in ensuring compliance during data handling and analysis, and your strategies for integrating compliance into everyday data science practices.

Describe specific big data technologies you've worked with, such as Pig or Hive, and how you've leveraged them to transform data analysis or enhance risk management capabilities.

Share your commitment to professional growth and what you hope to gain from mentorship opportunities, emphasizing your desire to continue developing your skills in data science and leadership.

Discuss your methodology for validating data sources, checking for accuracy, and implementing quality control measures throughout your data analysis process to ensure reliable results.