Senior Data Scientist​/MLOps Engineer

Job Summary

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 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 Dev Ops 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.

• 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.

• Experience with high-stakes, confidential use cases involving complex data modeling.
• Local to Minnesota (Strongly preferred for onshore collaboration).
• Ability to work in an agile, fast-paced environment with an "immediate" onboarding timeline.