Professional Overview
Stephanie is a strategic data science leader with a strong background in supply chain analytics, predictive modeling, and data-driven decision support. Her industry focus on supply chain optimization, combined with advanced technical skills in machine learning and big data analysis, allows her to deliver impactful solutions that drive measurable business outcomes.
Experience Summary
Current Role
In her current role at Mars, Stephanie is responsible for developing and implementing cutting-edge data science solutions to optimize the company's global supply chain operations. She leads a team of data scientists and analysts in leveraging sophisticated predictive models, forecasting techniques, and advanced analytics to enhance inventory management, demand planning, and logistics optimization. Stephanie's work has contributed to significant cost savings, improved service levels, and increased supply chain resilience for the organization.
Career Progression
Prior to joining Mars, Stephanie held several data science leadership roles, including Senior Data Scientist at NorCom Information Technology GmbH & Co. KGaA, Lead Data Scientist at BJSS, and Senior Data Scientist at Symphony Retail AI. She has a proven track record of driving successful data-driven initiatives, implementing advanced analytics strategies, and translating complex data insights into actionable business solutions.
Academic Background
Stephanie holds a Master's degree in Business Analytics from the University of London, where she specialized in supply chain optimization and predictive modeling. Her strong academic background, combined with her industry experience, enables her to bridge the gap between data science and business strategy.
Areas of Expertise
- Supply chain data analytics and optimization
- Predictive modeling and forecasting
- Machine learning and artificial intelligence
- Big data management and infrastructure
- Cross-functional collaboration and stakeholder management
- Strategic decision support and business consulting
Professional Impact
Throughout her career, Stephanie has delivered numerous impactful projects that have driven significant business value. At her previous role at BJSS, she led the development of a machine learning-based demand forecasting system that improved forecast accuracy by 25% and reduced inventory costs by 18%. At Symphony Retail AI, she spearheaded the implementation of a supply chain visibility and tracking platform, which enabled the company to achieve a 12% reduction in transportation costs and a 15% improvement in on-time delivery performance.
Conclusion
Stephanie Seiermann is a highly skilled and results-oriented data science professional with a proven track record of driving supply chain optimization and business transformation. Her deep expertise in supply chain analytics, combined with her strong leadership abilities and business acumen, make her a valuable asset in driving data-driven decision-making and delivering tangible business impact.
