Directors, Data Science - job 6 of 16

As a Director of Data Science at American Express, you will be responsible for driving the strategic analysis of credit metrics, overseeing credit-related analytical support, and presenting key insights to management. You will also identify trends within data to optimize business and risk decisions while managing specific data assets and applications.

To qualify for the Director of Data Science role at American Express, candidates need a Master’s degree in a relevant field such as Finance, Statistics, or Computer Science. Additionally, two years of experience in risk management and credit analysis, as well as expertise in data analysis, machine learning models, and data management processes are essential.

Directors of Data Science at American Express utilize a variety of tools and technologies, including Base SAS, SQL, R, and Big Data technologies such as Pig and Hive. They also work with advanced machine learning models, including logistic regression and GBM techniques, to analyze credit risk data efficiently.

Yes, American Express offers the flexibility of telecommuting up to two days per week for the Director of Data Science position, fostering a work-life balance while driving impactful analyses and strategies.

Directors of Data Science at American Express enjoy a competitive salary, bonus incentives, a robust retirement savings plan with company matching, comprehensive health benefits, paid parental leave, and opportunities for professional development. The company values holistic well-being via various programs.

In your response, discuss specific projects where you applied risk management principles, highlighting your analytical skills and decision-making. Provide examples of how you used data to mitigate risks or improve outcomes in past roles.

Focus on tools like SAS, SQL, or R. Discuss specific projects where you implemented these tools, detailing your role in leveraging them to derive insights from large datasets and the impact of your findings on business decisions.

Outline your process for data analysis, including how you prepare for presentations. Emphasize the importance of aligning your findings with business objectives, and share an example where your presentation influenced a strategic decision.

Discuss the types of models you have used, such as logistic regression or GBM, and relate them to how you have successfully implemented these models to enhance credit risk predictions or optimize decision-making in previous roles.

Demonstrate your analytical problem-solving skills by explaining your step-by-step approach to investigate discrepancies, emphasizing teamwork, and the importance of data integrity. Share a past example if applicable.

Mention reputable sources like journals, webinars, and conferences that you follow. Additionally, discuss how you apply new knowledge to your projects, showcasing your commitment to continual learning.

Highlight your teamwork experiences, specifying how collaboration enhances decision-making and innovation in projects. Share examples of successful cross-functional projects or initiatives you've been part of.

Discuss your methods for translating complex data analyses into clear, actionable recommendations that address specific business objectives, ensuring your stakeholders understand the potential impact of your insights.

Discuss your experience with credit risk data, detailing how you’ve analyzed this data to derive insights that drive business and risk decisions. Highlight specific metrics that have informed strategic choices in your past roles.

Emphasize your approach to training and supporting team members in using data science tools. Discuss any initiatives you've implemented to promote data literacy and encourage analytical thinking across teams.