Senior Managers, Data Science - job 3 of 3

As a Senior Manager, Data Science at American Express, your primary responsibilities include developing predictive models and utilizing machine learning techniques to enhance forecasts and generate business insights. You'll also collaborate with finance, risk, and business teams to uncover strategic insights, and research emerging technologies to ensure the implementation of successful quantitative analytical approaches.

To qualify for the Senior Manager, Data Science position at American Express, you need a Master’s degree in Statistics, Mathematics, Computer Science, Engineering, Cognitive Science, or a related field. Additionally, one year of experience in applying statistical and econometric modeling techniques is necessary, along with proficiency in SQL, A/B testing, and advanced predictive analytics.

In the Senior Manager, Data Science role at American Express, essential programming skills include proficiency in Python, R, and familiarity with data processing frameworks like PySpark. Hands-on experience with machine learning techniques, database queries using SQL, and knowledge of deep learning will also be pivotal for success.

American Express values work-life balance and offers its Senior Managers, Data Science the flexibility to telecommute up to two days per week. This policy is designed to help employees manage their professional lives effectively while maintaining personal commitments.

At American Express, a Senior Manager, Data Science can expect a comprehensive benefits package, including competitive salaries, performance-based bonuses, extensive medical, dental, vision, and life insurance options, as well as a robust retirement savings plan with company matching. Additionally, employees benefit from paid parental leave, financial coaching, wellness programs, and career development opportunities.

When answering this question, discuss specific predictive modeling techniques you've applied, such as regression analysis or decision trees. Highlight how you used these models to drive business insights and describe the impact of your work on decision-making.

Emphasize the importance of cross-functional collaboration. Share examples of past projects where you worked alongside finance and risk teams to ensure your data-driven insights aligned with business goals, and how communication and empathy played roles in fostering effective teamwork.

Identify key statistical techniques like linear regression, time series analysis, or A/B testing. Explain why you find these methods effective, providing examples of how you've successfully applied them to solve business problems or improve processes.

This question assesses your communication skills. Discuss strategies such as using visuals, analogies, or simplified language. Share a specific example where you successfully communicated technical information to stakeholders who may not have a data background.

Discuss specific machine learning techniques you've used, such as clustering or neural networks. Share an example of a project where you implemented these technologies to extract insights or improve predictive accuracy.

Mention resources like academic journals, online courses, webinars, or industry conferences. Highlight your commitment to continuous learning and possibly a recent topic or technology you’ve explored that could benefit the company.

This is an opportunity to showcase problem-solving skills. Describe a specific project, the challenges you faced (like data quality or stakeholder alignment), and detail your approach to overcoming those challenges, including any lessons learned.

Provide a specific instance where your SQL skills were essential. This could involve retrieving complex datasets or optimizing queries. Highlight the impact of your work, ensuring to connect it back to broader business objectives.

Discuss the importance of data visualization in presenting insights in a digestible format. Provide an example of a visualization you created that helped stakeholders make informed decisions or understand a complex issue.

Enumerate key performance indicators relevant to your projects, such as improvement in forecasting accuracy or business outcomes. Discuss how you measure success post-implementation and the importance of ongoing analysis.