Data Science Intern

Who are we, and what do we do?

At Corteva Agriscience, you will help us grow what's next. No matter your role, you will be part of a team that is building the future of agriculture. We’re leading breakthroughs at the intersection of chemistry and artificial intelligence—leveraging generative AI, large language models (LLMs), and advanced analytics to revolutionize crop protection product development, improving lives worldwide and driving progress for humanity.

Corteva Agriscience™ is seeking students for full-time internship within our AI and Data Science organization in Indianapolis, Indiana, working at the exciting crossroads of AI/ML and Chemistry. We offer students the opportunity to work closely with senior data scientists, ML engineers, chemists, and crop protection researchers in an industrial setting, with extensive opportunities to network across the company. This unique role focuses on applying cutting-edge AI technologies to accelerate formulation development.  Students are encouraged to be creative and take initiative, where appropriate, while working with their supervisor. Students are expected to always operate in a safe and efficient manner. Essential to this internship are interpersonal, organizational, communication, teamwork, and time management skills.

What You'll Do:

• Build LLM Agents: Create specialized LLM applications using domain-specific fine-tuning and RAG systems trained on chemistry literature, patents, safety data sheets, regulatory documents, and internal research data
• Apply AI/ML to Formulation Development: Develop and deploy generative AI and machine learning models to optimize formulations
• Automate Workflows: Create AI-powered tools to automate data extraction from experimental reports, literature mining for chemical information, and analysis of analytical chemistry data (NMR, MS, HPLC)
• Collaborate Across Disciplines: Partner with chemists, analytical scientists, formulation scientists, and agronomists to understand their challenges and develop AI solutions that address real-world crop protection problems
• Present Impact: Deliver project reviews throughout the summer and a final presentation showcasing how your AI/ML work has advanced crop protection chemistry research

Qualifications:

• Currently pursuing a Bachelor's, Master's, or Doctorate degree in Computer Science, Machine Learning, Artificial Intelligence, Computational Chemistry, Chemical Engineering, Chemistry, Material Science, Polymer Science or a related technical field
• Must have completed at least three years of undergraduate work before the start of the internship
• GPA of 3.0 or better
• Must be able to relocate to Indianapolis, Indiana for the duration of the internship
• Must be able to work full-time (40 hours per week) for at least 10 weeks during the timeframe of May to August

Required Technical Skills:

• Programming: Strong proficiency in Python with experience in AI/ML libraries (PyTorch, TensorFlow, scikit-learn, pandas, NumPy)
• LLMs & Generative AI: Hands-on experience with large language model APIs (OpenAI, Anthropic, Google, or open-source models) and understanding of LLM applications
• Prompt Engineering: Demonstrated ability in designing prompts for technical/scientific tasks and optimizing model outputs for domain-specific applications
• RAG Systems: Understanding of Retrieval-Augmented Generation concepts including vector embeddings, semantic search, chunking strategies for scientific literature
• Data Management: Proficiency with SQL and experience in data manipulation, cleaning, and preprocessing of complex technical datasets
• Statistical Analysis: Understanding of data structures, algorithms, statistical methods, and their application to experimental data
• Scientific Communication: Strong ability to communicate with both AI/ML experts and chemistry/crop protection domain specialists
• Problem-Solving: Analytical mindset with curiosity about both chemistry and AI, and willingness to learn domain-specific concepts

Preferred Skills:

AI/ML Expertise:

• Experience with LLM application frameworks (LangChain/Langgraph, LlamaIndex) and their application to scientific domains
• Knowledge of fine-tuning techniques (LoRA, QLoRA) for domain adaptation
• Familiarity with vector databases (Pinecone, Weaviate, Milvus, Chroma, FAISS)
• Experience with cloud platforms (AWS, Azure, GCP) and their AI/ML services
• Understanding of MLOps/LLMOps practices including CI/CD, version control (Git), and model deployment

Chemistry/Scientific Computing:

• Basic understanding of organic chemis