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CO-LOCATED EVENTS

Speaker

Zehra Erenkus

Lead, Global Commercial Data Science, Bayer AG

Zehra is a high-achieving manager leading customer/business/advanced analytics projects to reach targets. She takes an active role in top 3rd ranked companies gain a competitive edge and increase revenue by identifying customer needs, providing recommendations and implementing technology that solve problems and enhance capabilities. She holds predominant skills and demonstrated experiences on artificial intelligence, machine learning, data science, big data analysis, forecasting, modelling, segmentation, performance systems, clinical trials, CRM systems with using statistical programs in advanced (SAS, SPSS, Stata, R, SAP, DSS, Tableau, Spark Beyond, Phyton). She focusses on senior roles where she can combine communication skills and result oriented approaches to build business impact.

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Company

Bayer AG

https://www.bayer.com/en/
Bayer is a global enterprise with core competencies in the Life Science fields of health care and agriculture. Our products and services are designed to benefit people and improve their quality of life. At the same time, we aim to create value through innovation, growth and high earning power. Our products help address some of today’s biggest challenges, including global population growth, an aging society and the need to make efficient – and, wherever possible, sustainable – use of natural resources. In line with our mission “Bayer: Science For A Better Life,” we aim to improve people’s quality of life by preventing, alleviating or curing diseases. We also help provide an adequate supply of high-quality food, feed and renewable plant-based raw materials. For these endeavors, we focus on developing and successfully commercializing innovative products and solutions based on scientific knowledge.
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Colocated with
Big Data Minds Europe 2026 is part of the Event Series. You can find more events here.
Date
March 16 – 17, 2026
Location
Holiday Inn City Center
Hochstraße 3
81669 Munich
Phone
+49 (0)30 52 10 70 3 0
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