Data Scientist

Quantum Technologies. LLC; Minneapolis (MN); On-site; USD 120,000 - 190,000; Full time

Job summary

Quantum Technologies. LLC in Minneapolis seeks a data scientist with deep MLOps expertise to architect and productionalize complex optimization models, with a focus on simulated annealing and future quantum annealing initiatives.

You will design core models, build robust data and ML pipelines, ensure production-ready deployment, and collaborate with engineering to integrate the optimization engine into live systems.

Qualifications

• Expert in optimization algorithms, especially Simulated Annealing.
• Experience transitioning models from classical optimization to annealing approaches.
• Proven ability to write production-grade code and deploy models.
• Strong understanding of MLOps pipelines and production readiness.
• Familiarity with Azure-based ML pipelines is a plus.

Responsibilities

• Design and develop advanced optimization models with annealing techniques.
• Build and maintain robust ML pipelines for production deployment.
• Lead algorithm selection and justify architectural choices.
• Ensure scalable, reliable model deployment in live environments.
• Collaborate with engineering and DevOps teams to operationalize models.

Skills

Simulated Annealing

Quantum Annealing

MLOps

Optimization

Production-ready code

Tools

Azure

Job description

Banking & Finance,Utilities/ Energy,Telecom,Aerospace/ Airlines,IT/Software,Media/ Entertainment

Work Authorization:

Job 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.

Key Responsibilities

• 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.

Technical Requirements

• Advanced Optimization:Deep expertise in optimization algorithms, specificallySimulated Annealing. Familiarity or interest inQuantum Annealing/Quantum Computingis a significant plus.
• Engineering Excellence:Proven ability to writeproduction-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.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.