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 in a highly interactive and team-oriented environment;
• Deploy developed models into production and integrates 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;
• 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.

WHO YOU ARE

Your success will be driven by your demonstration of our LIFE values. More specifically related to this position, Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• Master's degree with at least two 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 cultures and backgrounds;
• Excellent work ethic, and a strong desire to learn new skills for technical and professional growth;
• 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, operation research and deep learning;
• Familiarity with common data science toolkits and data visualization tools (Shiny, Dash, Streamlit, PowerBI, Tableau).