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# Data Scientist

- **Company:** Robotics Technologies LLC
- **Location:** Minneapolis, MN, United States
- **Contract:** Temporary contract
- **Skills:** Microsoft Azure, DevOps, Quantum Computing, Model Validation, Optimization Algorithms, Production Code, Machine Learning Operations
- **Published:** June 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=90065685d7e90743

## About the Role

Do you have experience in Model evaluation?, * Advanced Optimization: Deep expertise in optimization algorithms, specifically Simulated Annealing. Familiarity or interest in Quantum Annealing/Quantum Computing is a significant plus.

 * Engineering Excellence: Proven ability to write production-ready code. This is not a research-only role; you must be able to "conscribe" and deploy your own work.
 * MLOps Frameworks: Strong experience in building and managing machine learning pipelines ( Azure preferred).
 * Data Science Fundamentals: Mastery of variable selection, algorithm tuning, and model evaluation metrics.

## Description

We are seeking a highly skilled Data Scientist with a strong background in MLOps Engineering to lead the development and productionalization of complex optimization models. You will be responsible for not only designing the core models specifically focusing on Simulated Annealing and transitioning toward Quantum Annealing, but also building the automated pipelines required to move these models into a production environment., * Core Modeling: Design and develop advanced optimization models. You will lead the "journey" from classical optimization to simulated annealing, with a future-state focus on quantum annealing.

 * MLOps & Productionization: Bridge the gap between data science and DevOps by writing production-grade code. Ensure models are scalable, reliable, and integrated into the broader ecosystem.
 * Pipeline Construction: Design and maintain robust data and ML pipelines. You will determine what data is pushed through the system and how it is processed for maximum efficiency.
 * Algorithm Selection: Lead the selection of algorithms and variables. You must understand how models "reason" and be able to justify the architectural choices for the optimization engine.
 * Deployment: Take full ownership of the model deployment lifecycle, ensuring that the "Optimization Thing" (internal use case) is fully functional in a live environment.


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