Senior Managers, Data Science - job 2 of 3

As a Senior Manager, Data Science at American Express, your primary responsibilities will include developing predictive modeling strategies to analyze customer behaviors, utilizing advanced machine learning to enhance forecasting, and generating valuable business insights. You'll have the opportunity to partner across various departments such as finance and risk to bring strategic insights through data visualization and reporting tools.

To qualify for the Senior Manager, Data Science role at American Express, candidates need a Master’s degree in Statistics, Mathematics, Computer Science, Engineering, or a related field. Additionally, at least one year of experience in statistical and econometric modeling techniques, including expertise in areas like regression analysis and machine learning is essential.

Yes, American Express offers flexible work arrangements for the Senior Manager, Data Science role, allowing for telecommuting up to two days per week, giving you the opportunity to maintain work-life balance while contributing to exciting projects.

Familiarity with SQL for database querying is crucial. Additionally, candidates should have experience using programming languages and tools such as R, Python, AWS, and techniques like A/B testing and deep learning to excel in this position within American Express.

The expected salary range for a Senior Manager, Data Science at American Express is between $111,966.00 and $165,000.00 annually, depending on relevant experience and location, accompanied by a comprehensive benefits package to support both your personal and professional growth.

When developing a predictive model, start by thoroughly understanding the business problem and gathering relevant data. After identifying key variables, I utilize exploratory data analysis to uncover patterns. From there, I select appropriate machine learning techniques and evaluate model performance, adjusting parameters to achieve the best results.

Absolutely! I'd start by breaking down the concept into simpler components, using relatable analogies. For example, explaining regression analysis could involve comparing it to predicting future sales based on past performance, emphasizing how various factors influence outcomes in a straightforward manner.

I find techniques like logistic regression for binary outcomes, decision trees for interpretability, and ensemble methods like random forests highly effective for predictive analytics. Each technique can provide unique insights, and often, using them in combination yields the best results.

Data quality is paramount in analytics. I ensure integrity by implementing thorough data cleansing procedures, validating source data, and conducting regular audits of the datasets to identify and address any anomalies or inconsistencies before analysis.

In a previous role, I presented a comprehensive analytics report to a group of stakeholders. I focused on visual aids, simplifying results into key takeaways, and engaged the audience with a Q&A session to clarify complex topics, ensuring thorough understanding while maintaining their interest.

Teamwork is essential in data science projects since collaboration across diverse departments unites different perspectives and expertise. Working together allows us to merge technical insights with business objectives, enhancing the overall impact of our analytics work.

I regularly engage with the data science community through webinars, online courses, and forums. I also subscribe to leading journals and blogs, which helps me stay updated on advancements and best practices, allowing me to integrate fresh techniques into my work.

To manage multiple projects effectively, I utilize project management tools to track progress and deadlines. Prioritization is based on business impact and urgency, and I ensure clear communication with stakeholders to align on expectations and deliverables.

One notable project involved developing a predictive model for customer churn. By accurately identifying high-risk customers, we implemented targeted retention strategies that resulted in a 20% decrease in churn rates. This significantly boosted revenue and improved customer satisfaction.

Failure is a learning opportunity in data science. I analyze the model's performance to identify weak points and gather feedback. This analysis often leads to adjustments in the data, feature selection, or model choice, improving future outcomes.