Response: Certainly! While my role has primarily been at a leadership and strategic level, I have closely collaborated with technical teams to fine-tune predictive models and ensure alignment with business objectives.  

One example involved addressing an underestimation issue in a demand forecasting model for a retail client. The initial model underestimated demand during promotional periods, leading to stockouts and missed revenue opportunities. To address this, I worked with the data science team to recalibrate the model by incorporating additional features, such as promotional calendars, regional buying patterns, and external factors like competitor activity.  

From a business perspective, I ensured that the recalibration aligned with the client’s objectives by defining clear KPIs, such as improving forecast accuracy by 15% and reducing stockouts by 20%. Regular workshops with stakeholders were conducted to validate the updated model’s outputs and ensure they met operational needs.  

On the technical side, I facilitated discussions to ensure the recalibration was feasible within the client’s existing infrastructure. For example, we optimized data pipelines to include real-time inputs and ensured the updated model could be deployed seamlessly without disrupting ongoing operations.  

By maintaining close collaboration between business and technical teams, we successfully fine-tuned the model, achieving greater accuracy and aligning it with the client’s strategic goals. This experience reinforced the importance of strong leadership in bridging the gap between business objectives and technical execution.