Directors, Data Science - job 7 of 16

The Director of Data Science at American Express is responsible for leading the analysis of credit metrics, providing analytical support for credit-related decisions, and presenting key findings to management. This includes identifying trends and opportunities in the portfolio to optimize business decisions. Additionally, the director will own specific data assets and ensure team members effectively use data science tools.

To apply for the Director of Data Science role at American Express, candidates must have a Master's degree in Finance, Statistics, or a related field, along with two years of experience in risk management and analysis. Significant knowledge in handling credit risk data, regulatory compliance, and familiarity with technologies like SAS, SQL, and advanced machine learning models is also required.

A Director of Data Science at American Express should be proficient in various technologies including Big Data tools like Pig and Hive, programming languages such as R, and analytics software like Base SAS and Teradata. Experience with machine learning models such as logistic regression and GBM techniques is also essential.

The salary range for the Director of Data Science position at American Express in New York is between $150,000.00 and $225,000.00 annually. Salary may vary based on experience and qualifications.

American Express provides a comprehensive benefits package for Directors of Data Science, including competitive salaries, bonus incentives, a retirement savings plan with a 6% company match, medical and dental insurance, generous parental leave, wellness support, and extensive career development opportunities.

When answering this question, focus on specific projects where you effectively analyzed credit risk data and the methodologies you utilized. Highlight your experience with credit bureaus and how you derived insights that led to measurable business impacts.

For this question, list tools like SAS, SQL, R, and any Big Data technologies such as Hive or Pig that you have experience with. Explain your level of proficiency with each and provide examples of how you've employed these tools in past projects.

Discuss your methodology for developing machine learning models. Emphasize understanding the business problem, performing exploratory data analysis, selecting appropriate algorithms (like GBM or logistic regression), and validating model performance, with specific examples from your experience.

In your response, highlight your ability to simplify complex data findings into a narrative that emphasizes key takeaways and actionable insights. Share a specific instance where you successfully communicated complex data to a non-technical audience.

Emphasize your organizational skills and mention any methodologies like Agile or Scrum that you follow. Give examples of how you prioritized tasks, met deadlines, and ensured quality control throughout your projects.

Provide a detailed account of a significant challenge you faced while working on an analysis project. Explain the steps you took to resolve it, what you learned, and how it improved your future work.

Talk about the resources you use to stay informed, such as online courses, webinars, industry publications, and networking with other data professionals. Mention any recent trends you have incorporated into your work.

Discuss the key metrics such as credit score, default rates, and exposure at default. Explain why these metrics are important and how they inform risk management strategies.

Share examples of how you've effectively used data visualization tools like Tableau or Power BI to present complex data in an accessible way. Mention how these visualizations aided decision-making processes.

Reflect on the aspects of data science that excite you, such as solving problems, generating insights that drive business strategy, or mentoring fellow team members. Personal anecdotes can enhance your answer.