Senior Managers, Data Analytics - job 1 of 4

As a Senior Manager, Data Analytics at American Express, your primary responsibilities include analyzing market trends, identifying growth opportunities across card portfolios, and developing strategies to enhance customer engagement. You'll track performance metrics for a diverse range of customers to connect their priorities with American Express's offerings, ensuring effective strategy execution within an existing portfolio.

To qualify for the Senior Manager, Data Analytics position at American Express, candidates need a Master's degree in fields such as Business Administration, Finance, or Statistics. Additionally, a minimum of one year of relevant experience is required, including expertise in data analysis and statistical techniques. Proficiency in MS Excel, SQL, and programming languages is also essential for success in this role.

Success in the Senior Manager, Data Analytics role at American Express mandates strong analytical skills, expertise in data modeling, and proficiency with tools such as SQL, Python, and R. Excellent communication skills are crucial for effectively coordinating with internal business partners and enhancing client experiences across various portfolios.

Yes, American Express provides the flexibility of telecommuting for Senior Managers, Data Analytics, allowing you to work remotely up to two days a week. This option is part of their commitment to work-life balance and employee well-being.

As a Senior Manager, Data Analytics at American Express, you'll enjoy a comprehensive range of benefits, including competitive salaries, bonus incentives, a generous retirement savings plan with company matching, and 20+ weeks of paid parental leave, among others. Moreover, the company prioritizes your holistic well-being with various health and financial support programs.

In interviews for the Senior Manager, Data Analytics position, be prepared to discuss your analytical framework. Describe how you leverage tools like SQL and Python to extract insights from data, analyze trends, and inform strategic decisions. Having a specific example ready will help illustrate your analytical approach effectively.

When answering this question, emphasize your familiarity with statistical techniques and tools used in time-series analysis. Provide examples of how you've applied these methods in real scenarios to help drive decision-making and identify spending trends within portfolios.

Share a specific experience where your data insights led to a successful growth strategy. Highlight the challenges faced, the analytical steps you took, and the outcome of implementing your strategy to provide a real-world context.

In responding to this question, discuss your methodology for assessing and prioritizing accounts. Explain how you factor in customer potential, spending history, and overall business impact, illustrating your decision-making process with relevant examples.

Prepare to discuss your preferred data modeling tools, such as MS Excel, R, or SAS. Explain why you prefer these tools based on their strengths in generating reports and forecasts, and how they align with the objectives of the Senior Manager, Data Analytics role.

Provide an example that showcases your collaboration with various teams within a company. Discuss how you harmonized different priorities and perspectives to achieve a common goal, emphasizing your communication skills throughout the process.

Share your approach to continuous learning. Mention sources like industry publications, webinars, professional networks, and conferences that you engage with to stay informed about the latest trends and technologies in data analytics.

Answer this question by detailing a specific project where your analysis contributed significantly to a company's success, such as increasing revenue, reducing costs, or enhancing customer satisfaction. Be clear about your role and the skills you applied.

Discuss your problem-solving strategy when faced with conflicting data. Explain your process for validating data sources, revisiting analytical methods, and consulting with team members to resolve discrepancies, demonstrating your critical thinking capabilities.

Detail your experience using various machine learning tools, specifying which ones you have utilized (e.g., SAS, Python). Discuss projects where you applied machine learning techniques to improve data insights and influence business decisions.