Sr Data Scientist, AI

Bayer•Chicago, IL

About The Position

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. Sr Data Scientist, AI Here at Bayer Digital Farming, we are seeking a highly motivated Senior Data Scientist who shares our passion for innovation to join our team within the Science organization. You will have the opportunity to be on the forefront of developing novel & complex integrated agronomic models for recommendations on seeds, agronomic traits, and crop protection that farmers need today. Our team has access to rich, industry-leading data sources to drive creation of predictive and prescriptive models that will shape the future of the agriculture industry. As part of our diverse, highly dynamic group, you will work side-by-side with a team of exceptional scientists, data engineers, analysts, and others with diverse backgrounds to deliver next-generation scientific breakthroughs, while fostering your career growth and development. If you are passionate about addressing feeding a growing planet sustainably, with a direct impact on farmers, Bayer would love to talk to you. We work on hard problems because we love the challenge! As a Senior Data Scientist, you’ll play a key role in delivering innovative scientific breakthroughs by applying predictive analytics, risk optimization, and statistics to integrated recommendations. You will work closely with interdisciplinary scientists, domain experts, and stakeholders to predict the product performance and provide personalized insights for growers while developing novel analytical models and best practices for complex data analyses.

Requirements

PhD +2 years or MS +5 years of experience in Statistics, Mathematics, Computer Science or related field
Minimum 3-5 years of experience in applying statistical/machine learning solutions on a scalable platform to real-world applications
Fluency in a programming language commonly used for data manipulation and computational statistics, such as Python (required) and SQL (preferred)
Mastery of multiple regression techniques, multivariate analysis, and mixed models
Proven experience with software development best practices such as modularity, version control, documentation, code review, and automated test design
Proven experience building generative AI agents tuned to custom datasets that deliver actionable insights, and deploying them in scalable solutions that ensure trustworthy, high-quality results
Strong communication skills and the ability to work with diverse cross-functional teams, including examples of providing quality feedback and helping others to develop
Domain expertise in Agronomy, Plant Genetics, Plant Pathology, Crop Physiology, Remote Sensing or related discipline

Nice To Haves

Experience in developing agronomic models, including the effects of environment, management, and genetics on crop outcomes
Theoretical knowledge and practical experience with advanced statistical and machine learning analysis techniques
Proficiency in programming computational and statistical algorithms for complex data sets
Track record demonstrating excellent command of research questions and critical thinking skills
Proven experience using multiple forms of AI such as machine learning, deep learning, tensor flow, neural networks or predictive analytics to drive decisions using new algorithms efficiently and rigorously

Responsibilities

Explore, analyze, and model large and diverse agronomic datasets
Design and develop models using statistical modeling, process modeling, and machine learning
Apply sound statistical principles, selecting the most appropriate modeling techniques and data visualization, applying innovative methods where they add value
Articulate project plans and architecture, independently design technical approaches, and model limitations and advantages, defending and communicating conclusions for technical and non-technical audiences
Follow software development best practices and produce code that anticipates scaling and production, assisting with code review and providing expert consultation
Use your domain knowledge to anticipate and proactively address concerns and solutions, identifying opportunities for value creation
Work in close collaboration with teams of world-class scientists, engineers, analysts, and business stakeholders, understanding customer pain points, identifying alternative solutions and strategies, and influencing others to drive impact