Data Science Internship:
Want to work at the forefront of artificial intelligence and agriculture? In partnership with Cargill, through the University of Illinois, Urbana Champaign (UIUC) research park, you will be given the opportunity as a graduate level intern to apply knowledge gained in the classroom to a real-life environment and then multiply by tenfold. With Cargill’s significant presence across agricultural supply chains, that large footprint comes massive amounts of data that can inform us about markets and ways to improve our business practices, particularly in the area of safety. Safety is a top priority for Cargill. It’s right there in our purpose statement “to nourish the world in a safe, responsible and sustainable way”. We are applying advanced techniques to make this a reality.
As a Data Science Intern, from day one, you will be an integral part of the team working with the engineering and data science teams, and will tackle real challenges, cultivate your curiosity, have client exposure, enjoy both personal and team accomplishments and have your initiative acknowledged along the way. You will collaborate and build relationships with colleagues and clients who represent diverse work, culture and resolution styles. We look for people who want to grow, support, think and produce.
Your project will connect you back to the business where you will interact with a multidisciplinary team. You will bring strong technical skills to our data science capabilities. You will explore, connect, and mine data; plus develop models using algorithms for pattern detection. In this position you will be managing a project that uses NLP, social network analysis, and prediction algorithms, to develop solutions that help deliver significant value to Cargill’s businesses and functions by providing safety insight.
- 20% - Work in a cross-disciplinary project team of software engineers, database specialists, data scientists, and business subject-matter experts to develop a project plan and deliverables, plus communicate technical solutions to a non-technical audience.
- 10% - Design strategies and propose algorithms to analyze and leverage data from a variety of sources.
- 70% - Develop and code models by applying algorithms to large structured as well as unstructured data, completing project deliverables
Job Location:
- University of Illinois Urbana-Champaign in Champaign, IL
Required Qualifications:
- Must be currently enrolled in a Masters or PhD program at the University of Illinois, Urbana Champaign in Data Science, Machine Learning, Computer Science, Computational Linguistics, Statistics, Mathematics, Engineering, Physics, or related fields with a graduation date after December 2019
- Able to complete a minimum 12-week internship in the summer (May/June - August 2019) or a semester long internship in Fall 2019 (lAug – December 2019).
- Experience developing and testing machine learning or statistical projects.
- Experience with various feature engineering and selection techniques, e.g., binning, PCA, t-SNE, transformations, etc.
- In-depth knowledge of various NLP modeling algorithms
- In-depth knowledge of various other modeling algorithms, e.g., regression, trees-based models, neural networks, ensembles, etc.
- In-depth knowledge of model deployment, hyperparameter tuning, and performance monitoring
- Proficiency in Python (e.g., pandas, scikit-learn, bokeh, matplotlib, NumPy, nltk), R (e.g., ggplot2, cluster, dplyr, caret), or other languages.
- Ability to understand complex and ambiguous business needs and apply the right tools and approaches.
- Curious, self-motivated, driven, and have a passion for problem solving.
- Collaborative team player.
- Excellent communication skills, both written and verbal.
- Strong presentation skills. Ability to present technical solutions to non-technical persons in an easy to understand way.
Preferred Qualifications:
- Experience in agriculture, commodity, or manufacturing businesses.
- Experience with weather and geospatial data.
- Experience with social network analysis.
- Experience working in a cloud environment e.g., Amazon Web Services.
Experience with Big Data development in Hadoop and Spark frameworks