Senior Managers, Data Analytics - job 4 of 4

As a Senior Manager, Data Analytics at American Express, your primary responsibilities will include analyzing market trends to identify growth opportunities, developing growth strategies for existing portfolios, and engaging with high-potential customers. You'll also be tracking portfolio performance and collaborating with internal teams to enhance the customer experience, ensuring our strategies effectively connect business priorities with American Express’s value proposition.

To apply for the Senior Manager, Data Analytics at American Express, you must possess a Master’s degree in Business Administration, Finance, Marketing, or related fields. Additionally, you need at least 1 year of experience in data and business analysis, including skills such as SQL, predictive modeling, and proficient use of tools like MS Excel and SAS to analyze data and provide insights.

American Express offers a robust benefits package that prioritizes employee well-being. As a Senior Manager, Data Analytics, you will enjoy competitive salaries, bonus incentives, a retirement savings plan with a 6% company match, comprehensive health insurance, and extensive parental leave. There's also access to wellness centers and confidential counseling support to ensure a healthy work-life balance.

Yes, American Express offers flexibility with telecommuting for the Senior Manager, Data Analytics position. You can work remotely up to two days per week, allowing for a better work-life balance while still collaborating effectively with your team and contributing to our business objectives.

In your role as a Senior Manager, Data Analytics at American Express, you'll utilize a range of tools and technologies, including SQL for database management, MS Excel for data analysis and modeling, and software like SAS for predictive analysis. Programming knowledge in Hive, Python, R, and VBA will also be valuable for structuring data and developing insights that drive strategic decision-making.

When answering this question, focus on specific projects where you analyzed data to derive insights. Describe the tools you used, such as SQL or Excel, and the outcomes of your analysis. Highlight any strategic decisions that were influenced by your findings and how they contributed to business growth.

Outline your methodology when developing growth strategies, starting from data collection and cleaning to analyzing trends and insights. Discuss how you prioritize opportunities based on data and align them with organizational goals. Providing specific examples can help illustrate your approach.

Share examples of projects where you utilized predictive modeling and machine learning tools, including which specific techniques you employed. Highlight how these skills helped in deriving insights that influenced business decisions. Discuss any tools like SAS or Python you’ve worked with, emphasizing your hands-on experience.

Discuss your communication strategies and previous experiences where you worked closely with sales or marketing teams. Explain how you gathered feedback from these teams and used it to inform data-driven strategies. Emphasize the importance of a collaborative approach in enhancing customer satisfaction and achieving business goals.

Choose a specific project that posed unique challenges and explain the situation clearly. Discuss the steps you took to address those challenges and what data analysis techniques you employed. Focus on your problem-solving skills and any collaborative efforts you engaged in to overcome obstacles.

Talk about key performance indicators (KPIs) that you consider important for evaluating customer portfolios, such as customer retention rates, average spend, and engagement levels. Discuss how you collect and analyze these metrics to ensure that marketing and strategic efforts are effective.

Describe the analytical frameworks you would apply when reviewing market trends. Mention specific techniques such as time-series analysis or report generation, and explain how you would use the results to identify risks and opportunities for business growth.

Share your experiences working on cross-functional teams and highlight specific roles you played in contributing to joint projects. Explain how you navigated diverse team dynamics and how this collaboration enhanced the end results.

Discuss your methods for staying informed, such as attending workshops, webinars, or reading industry publications. You could mention any relevant certifications you’re pursuing or online courses you are taking to enhance your skills and knowledge in data analytics.

Choose a specific example where your data analysis directly impacted business performance. Describe the analysis conducted, the insights derived, and the actions taken following your recommendations. Discuss the results achieved and how they benefitted the organization.