Consumer Product Strategic Analyst IV - Strategy Validation - Credit Assistance

As a Consumer Product Strategic Analyst IV at Bank of America, you will perform complex analyses related to portfolio risk and profitability, manage multiple large-scale projects, and provide strategic insights regarding collections strategies. You'll also mentor less experienced analysts in SAS coding and analytical skills, while collaborating with cross-functional teams to enhance collections performance.

To qualify for the Consumer Product Strategic Analyst IV role at Bank of America, candidates should have at least 3 years of experience using SAS Enterprise Guide and SQL for data analysis. Proficiency in advanced SAS coding techniques, a solid understanding of collection strategies, and hands-on experience with large-scale data management systems are also crucial. A knack for problem-solving and leadership experience will set you apart!

The Consumer Product Strategic Analyst IV plays a pivotal role in improving Bank of America's business performance by analyzing financial models and market trends to optimize collections strategies. Your work will inform data-driven decision-making processes, support performance management, and lead initiatives that drive responsible growth across the organization.

As a Consumer Product Strategic Analyst IV at Bank of America, you will leverage advanced analytics tools, primarily SAS and SQL, coupled with data management systems like Hadoop, Teradata, and Oracle. Your role will demand expertise in these technologies to develop reports, dashboards, and insights that enhance collections performance and portfolio management.

In your role as a Consumer Product Strategic Analyst IV at Bank of America, you can expect to work on various significant projects such as platform conversions, analytics-based insights for collections efficiency, and delivering advanced reporting solutions. You’ll collaborate with Risk, Strategy Owners, and Analytics teams to drive strategic improvements and achieve optimal performance outcomes.

In my previous roles, I utilized SAS coding extensively for data analysis, focusing on financial models and portfolio trends. I developed complex reporting systems and predictive analytics to enhance performance metrics, which directly contributed to strategic decision-making.

I believe in a hands-on approach to mentoring. For example, I provide personalized training sessions focusing on SAS programming and data analysis best practices, while also encouraging juniors to tackle real-world projects under my guidance to build their confidence and proficiency.

Certainly! I led a project aimed at developing a new collection strategy tool that utilized SQL and SAS for data mining. It involved extensive cross-functional collaboration to collect insights and optimize our approach, ultimately leading to improved collection rates.

I use a combination of quantitative and qualitative analyses to evaluate collections performance. By leveraging advanced SAS reporting and SQL queries, I can track and assess metrics, identifying trends and actionable insights to refine our strategies continuously.

I regularly participate in webinars, industry conferences, and online courses to enhance my skills and stay informed of the latest trends in data analytics and risk management. Additionally, I follow industry publications and forums where professionals exchange insights and best practices.

To manage tight deadlines and multiple projects effectively, I prioritize tasks based on urgency and complexity. I believe in maintaining clear communication with team members and stakeholders, ensuring that we stay aligned and on track to meet our objectives.

I've had substantial experience collaborating with cross-functional teams, especially in driving data-driven projects. By fostering open lines of communication and promoting a collaborative environment, I ensure that different perspectives contribute to enhancing project outcomes.

In a previous role, I presented complex data findings regarding portfolio risk to senior management. I prepared a data visualization dashboard to simplify the analysis and focused on key insights that aligned with business objectives, which led to actionable strategies being implemented.

When making recommendations based on data analysis, I emphasize a thorough understanding of the data context. I ensure that my recommendations are not only data-driven but also align with strategic company goals, presenting them clearly to gain stakeholders' buy-in.

Assessing risk triggers in collection strategies involves analyzing historical data patterns and current portfolio conditions. I utilize predictive analytics models to identify potential risks, allowing us to implement proactive measures in our collection approaches.