Data Scientist II
Job Purpose and Impact
Want to push the limits of food and agriculture using artificial intelligence? Cargill has a significant presence across agricultural supply chains. With that footprint comes massive amounts of data that can inform us about markets, business practices, and research efforts. This Data Scientist position will be part of the Engineering and Data Sciences team and will bring strong technical skills to our data science capabilities. You will work on a multidisciplinary team exploring, connecting, and mining data. You will develop models using algorithms for pattern detection and optimization. In this position you will be working on projects that use advanced analytics and machine learning techniques to develop solutions that help deliver significant value to Cargill. Candidates will be exposed to a wide spectrum of Cargill manufacturing and operations problems.
You will develop and code models by applying algorithms to large data sets from a variety of sources. You will explore data, develop models, tune for accuracy, and put models into deployment.
You can expect to translate complex and ambiguous business problems into project charters, clearly identifying technical risks and project scope. You'll network with business partners to develop projects aligned with business strategies. And ensure strong communication of technical solutions to non-technical audiences.
On our multidisciplinary team of data scientists, data engineers, software engineers, and business subject-matter guides, you'll focus on delivering data science models on-time and in budget. If you like to continuously seek out best practices and develop skills to build new capabilities for data science, this role would be a good fit for you.
• Bachelor’s degree in Data Science, Computer Science, Math, Engineering or related field
• Industrial data science experience
• Knowledge of feature engineering and selection techniques (binning, PCA, t-SNE, transformations, etc.)
• Expertise in at least two algorithm types (e.g., regression, tree-based models, neural networks, ensembles, clustering, time series, reinforcement learning, NLP, probabilistic programming, etc.)
• Proficiency in Python (e.g., pandas, scikit-learn, numpy, bokeh)
• Proven ability to present to non-technical audiences
• Proven knowledge of model tuning, deployment, and monitoring
• Fluency in English
• Expertise in operations research and solving complex business optimization problems, including the use of tools such as CPLEX, Gurobi, or similar tools where you helped develop and tune the optimization formulation. Examples of these problems are traveling salesmen problems, inventory optimization, and production planning/scheduling.
• Experience in agriculture, commodities, or other manufacturing
• Experience in databases, Hadoop, and distributed computing frameworks
• Strong SQL
• Experience in software development environment and code management/versioning (e.g., git)
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