Directors, Data Science - job 12 of 16

As a Director of Data Science at American Express, you will primarily focus on driving strategic analyses related to credit metrics. This involves providing analytical support, identifying trends, and making data-driven recommendations to optimize business and risk decisions. You'll also be responsible for managing data assets, training team members, and utilizing data science tools effectively.

To qualify for the Director of Data Science role at American Express, you need a Master's degree in a relevant field such as Finance, Computer Science, or Statistics, along with two years of experience in risk management and analysis. Moreover, expertise in Big Data technologies, various regression models, and data management processes are essential to succeed in this role.

Yes! The Director of Data Science position at American Express allows for up to two days of telecommuting each week. This flexibility enables you to maintain a healthy work-life balance while still contributing significantly to a high-impact role.

A successful Director of Data Science at American Express should possess strong analytical and data management skills, proficiency in Big Data technologies, and experience with advanced machine learning models. Excellent communication skills to present findings and collaborate with teams across various locations are also crucial.

In the Director of Data Science role at American Express, you will engage in projects that focus on analyzing credit metrics, optimizing risk decisions, and implementing strategic guidance on risk information systems. You'll also work on enhancing data capabilities, aligning data science applications with business needs, and driving innovation throughout our analytical processes.

When answering this question, focus on specific projects or tasks where you've analyzed credit risk data, detailing the methodologies you used and the outcomes achieved. Highlight any experience with internal credit sources or credit bureaus and how your analyses contributed to making more informed risk decisions.

Discuss the specific data science tools and technologies you've used, such as SQL, SAS, or machine learning frameworks. Emphasize how you apply these tools to solve real-world problems and how they aid in decision-making and optimizing processes.

Provide examples of machine learning models you have developed or implemented, such as logistic regression or kNN. Explain the context, purpose of the model, and how the outcomes influenced business or risk strategies. Be prepared to discuss your approach to model validation and performance metrics.

Mention the resources you follow, such as industry publications, webinars, or professional networks that help you stay current. Highlight any online courses or certifications you've pursued recently to enhance your skill set and adapt to evolving trends.

Explain your strategy for simplifying complex analyses, using visuals and accessible language to convey insights effectively. Discuss the importance of storytelling in data presentation and how you tailor your approach based on your audience.

Be honest about specific challenges you encountered, such as data quality issues or resistance to change within teams. Discuss how you approached these challenges, the solutions you implemented, and the lessons learned from those experiences.

Explain your time management and prioritization techniques, detailing how you assess project urgency and importance. Provide an example of a time you successfully managed conflicting priorities and met deadlines without sacrificing quality.

Share a specific instance where you identified an inefficiency in a data process, describing the steps you took to improve it and the measurable outcomes. This demonstrates your proactive approach and capability in driving continuous improvement.

Discuss your passion for data science and the impact it has on strategic decision-making, especially in the context of risk management. Share personal anecdotes or aspirations that have drawn you to this field and how you see yourself contributing to American Express.

Mention the importance of open communication and collaboration in addressing disagreements. Discuss your approach to listening to differing viewpoints, using data to support your arguments, and working together toward a consensus that aligns with business objectives.