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# ITQ Data Scientist II

- **Company:** General Mills
- **Location:** Minneapolis, MN, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Microsoft Azure, Cloud Computing, Data Presentation, Python (Programming Language), Machine Learning, NumPy, Tensorflow, SQL Databases, Tableau (Software), Unstructured Data, Feature Engineering, Pytorch, Large Language Models, Deep Learning, Keras, Git, Pandas, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Virtual Agents
- **Published:** September 23, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pm2yrvbw0m

## About the Role

Education: Master's or Ph.D. in Data Science, Computer Science, Statistics, or related quantitative field

Experience: 5+ years building and deploying ML models, * Strong ML expertise (feature engineering, validation, ensemble models, neural networks)

 * Proficiency in Python/R (pandas, NumPy, scikit-learn, etc.)
 * Experience with PyTorch, TensorFlow, Keras, or similar
 * Hands-on MLOps, Git, and cloud platforms (AWS/GCP/Azure)
 * Experience deploying production ML systems
 * Strong data storytelling and visualization skills (Shiny, Dash, Tableau)
 * Ability to manage multiple projects independently

Preferred Qualification

 * 5+ years of experience
 * Background in statistics or quantitative sciences
 * Certifications in R, Python, or SQL

## Description

The Data Scientist II will design, build, and deploy scalable machine learning and AI solutions to solve complex R&D, consumer research, and quality challenges. This role requires deep expertise in ML, Deep Learning, MLOps, and Agentic AI systems, with the ability to independently lead end-to-end projects., Technical Excellence (70%)

 * Lead full ML lifecycle: problem framing * data prep * modeling * deployment
 * Build predictive models (regression, classification, clustering, time-series)
 * Apply statistical and advanced analytics to structured & unstructured data
 * Develop scalable ML pipelines and deploy models to production
 * Design and implement AI agents using LLMs, RAG, memory, tools, and orchestration frameworks
 * Evaluate and optimize AI systems using custom metrics and feedback loops
 * Stay current with emerging ML and GenAI advancements

Business Partnership (15%)

 * Translate business needs into analytical solutions
 * Communicate insights clearly to technical and non-technical stakeholders
 * Deliver projects on time with defined success criteria

Innovation & Continuous Improvement (10%)

 * Improve processes and methodologies
 * Develop new analytical capabilities
 * Continuously upskill in ML and AI best practices

Administration (5%)

 * Complete required trainings and organizational responsibilities


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