At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

 

Data Scientist 

 

Position Overview

 

As a Data Scientist, you will develop and deliver practical, interpretable, and automated solutions for business problems through predictive modeling, statistical analysis, simulation, and optimization in a highly interactive and team-oriented environment. You will deploy developed models into production and integrate them with existing infrastructure, systems, and platforms. Your role will involve preparing documentation, including detailed problem formulation, data requirements, modeling approach, and implementation steps. You will present data-driven insights, recommendations, and solutions to a broad range of audiences, including peers, senior management, and internal customers in a clear and interpretable manner. You will work closely with global data science teams and business partners to break down complex business problems and build feasible solutions that are impactful, accurate, and validated. Additionally, you will build cross-functional relationships with the business and collaborate effectively within the Global Data and Data Science Communities. As a proactive member of a global data science team, you will adopt and advocate for best practices.

 

YOUR TASKS AND RESPONSIBILITIES

 

The primary responsibilities of this role, Data Scientist, are to:

 

• Develop and deliver practical, interpretable, and automated solutions for business problems through predictive modeling, statistical analysis, simulation, and optimization;
• Deploy developed models into production and integrate with existing infrastructure, systems, and platforms;
• Prepare documentation including detailed problem formulation, data requirements, modeling approach, and implementation steps;
• Present data-driven insights, recommendations, and solutions to a broad range of audiences including peers, senior management, and internal customers in a clear and interpretable manner;
• Be creative, resourceful, detail-oriented, and able to take initiative when it comes to solving business problems;
• Work closely with global data science teams and business partners to break down complex business problems to build feasible solutions that are impactful, accurate, and validated;
• Build cross-functional relationships with the business and collaborate effectively within the Global Data and Data Science Communities;
• Be a proactive member of a global data science team in adopting and advocating for best practices.

 

WHO YOU ARE

 

Bayer seeks an incumbent who possesses the following:

 

Required Qualifications:

 

• Master’s degree with 2+ years of experience or a PhD in one of the following areas: Data Science, Electrical/Industrial Engineering, Applied Mathematics, Statistics, Computer Science, or other related quantitative discipline;
• Solid theoretical background in machine learning and statistical methods;
• Strong proficiency in Python and/or R programming languages;
• Hands-on experience building machine learning models and performing statistical analysis using Python/R;
• Proficiency in using SQL to access data (BigQuery preferred);
• Demonstrated working experience with real-world problems, preferably related to agriculture, genetic and environmental data;
• Good communication and presentation skills;
• Ability to work independently, in a matrix, and in a cross-functional team, with people from a variety of different backgrounds;
• Ability to handle multiple simultaneous projects, be highly organized, and prioritize to meet deadlines;
• Familiarity with version control and coding best practices.

 

Preferred Qualifications:

 

• Previous domain knowledge in Agriculture/Crop Science;
• Practical experience with Ensemble Methods, SVMs, Clustering, Outlier detection, Bayesian Methods;
• Knowledge in Natural Language Processing, Operations Research, and Deep Learning;
• Familiarity with common data science toolkits and data visualization tools (Shiny, Dash, Streamlit, PowerBI, Tableau).

 

Employees can expect to be