Senior Director, Global Forecasting - Job ID: SDGF2025

The Senior Director, Global Forecasting at Ascendis Pharma is responsible for leading the development of robust forecasting models that predict demand, revenue, and other crucial metrics. This role entails managing global supply and patient demand forecasts, adjusting parameters for top-down predictions, and collaborating with various marketing and business leads to ensure accuracy across all markets.

To qualify for the position of Senior Director, Global Forecasting at Ascendis Pharma, candidates should possess a bachelor's degree in an analytical field, with a master’s degree in business or life sciences preferred. Additionally, an extensive background in pharmaceutical forecasting, finance, analytics, or supply chain management, with a minimum of 10 years of relevant experience and at least 5 years in a leadership role, is essential.

Ascendis Pharma emphasizes continuous improvement in forecasting methodologies. The Senior Director, Global Forecasting will regularly track forecast performance against actual results, adjusting models as necessary to improve accuracy. The role also involves collaborating across functions to align forecasts with business strategies and local market dynamics.

Strong analytical skills, knowledge of forecasting principles, and proficiency in advanced Excel modeling are essential for the Senior Director, Global Forecasting at Ascendis Pharma. Excellent communication and presentation skills, stakeholder management abilities, and an aptitude for handling ambiguity are also crucial for success in this leadership role.

At Ascendis Pharma, we prioritize the growth of our employees, particularly in the Senior Director, Global Forecasting role. We offer diverse professional development programs, access to industry best practices, and continuous training on advanced analytics and technology to enhance forecasting capabilities.

In my previous roles, the top-down forecasting approach involved analyzing overall market trends and then breaking down those trends by regions or segments. This contrasts with the bottom-up approach, where we examined granular data points from individual markets to build our forecasts. I find that a combination of both usually yields the most accurate and robust forecasting models.

To ensure alignment, I prioritize building strong relationships with cross-functional teams, scheduling regular meetings to discuss assumptions and forecast methodologies. Utilizing collaborative platforms also helps in sharing updates and getting feedback, which aids in refining forecasts before rolling them out to senior leadership.

In one instance, we encountered unexpected regulatory changes that shifted market dynamics. I swiftly gathered data from local teams, analyzed the impact on our forecasts, and adjusted our models accordingly. Communicating these changes transparently with stakeholders was crucial to ensure everyone remained informed and aligned.

I've utilized various tools like Excel for complex modeling, along with dedicated forecasting software such as SAP Integrated Business Planning. Additionally, I often leverage advanced analytics tools for data mining and scenario modeling to enhance our forecasting capabilities.

I implement a routine review process where we compare forecasted results with actuals. Analyzing variances regularly helps us understand the reasons behind discrepancies, allowing for continuous adjustment and refinement of our forecasting models.

To communicate forecasting results effectively, I develop comprehensive presentations that outline key findings and visual data illustrations. I ensure that I highlight the implications for business strategy, making it easier for stakeholders to understand and act on the insights.

When forecasts don't meet expectations, I address the situation proactively. I review the data to understand why and communicate transparently with stakeholders about the findings. It's essential to highlight how we can pivot or adjust strategy based on these insights.

I've driven improvements by integrating advanced analytics and machine learning algorithms into our forecasting processes. This transition enabled us to analyze vast datasets more efficiently, leading to more accurate and timely forecasts tailored to market needs.

I focus on building relationships and trust with stakeholders by involving them early in the forecasting process. Consistently sharing insights and results fosters a sense of ownership, making it easier for them to align with and support the forecasts I present.

Market insights are a cornerstone of my forecasting process. I prioritize gathering both quantitative data and qualitative feedback from teams on the ground. This comprehensive understanding of market dynamics enables me to develop forecasts that are not only accurate but also responsive to real-world changes.