Data Scientist

System One is seeking a Data Scientist to design, build, and deploy production-grade machine learning models, bridging data science with software engineering to embed AI capabilities into enterprise products and systems in Minneapolis, onsite.

Location

Minneapolis, MN (onsite)

Role Overview

In this role, you will design, build, and deploy production-grade machine learning models while bridging data science with software engineering. The work focuses on delivering clean, modular Python code to embed AI capabilities within enterprise products and systems.

Responsibilities

• Design, train, and validate predictive and analytical models across regression, classification, decision trees, time-series, and neural networks.
• Implement scalable Python code to package models as microservices and expose them via REST APIs for real-time inference.
• Create and maintain automated data preprocessing, feature engineering, and model evaluation pipelines aligned with CI/CD and MLOps best practices.
• Monitor production models for performance, data drift, and latency to ensure ongoing reliability and accuracy.
• Collaborate with executive leadership, product directors, and enterprise architects to shape the AI and ML product roadmap.

Requirements

• 5 to 7 plus years of hands-on experience developing and sustaining complex ML/AI solutions within enterprise production environments.
• Experience deploying ML models into production settings.
• Proficiency with core ML algorithms and frameworks such as scikit-learn, XGBoost, LightGBM, statsmodels, PyTorch, or TensorFlow.
• Proven ability to deliver clean, modular, and object-oriented Python code that adheres to PEP 8 beyond notebook environments.
• Experience with automated testing frameworks (pytest, unittest) and implementing unit and integration tests.
• Practical experience building RESTful APIs with FastAPI or Flask.
• Experience with Docker and version control workflows using Git, GitHub Actions, or GitLab CI/CD.
• Advanced SQL skills and experience querying structured and unstructured data.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field.

Technologies

• Python
• scikit-learn
• XGBoost
• LightGBM
• statsmodels
• PyTorch
• TensorFlow
• FastAPI
• Flask
• Docker
• Git
• GitHub Actions
• GitLab CI/CD
• SQL

Benefits

• Health and welfare benefits coverage options including medical, dental, vision
• Spending accounts
• Life insurance
• Voluntary plans
• 401(k) plan

Preferred Qualifications

• Generative AI and Enterprise LLMs: hands-on experience developing, fine-tuning (LoRA/QLoRA), or deploying proprietary Large Language Models (LLMs), agentic frameworks, custom embeddings, or enterprise RAG systems.
• Experience with GPU acceleration frameworks (CUDA, TensorRT, vLLM, Ollama).
• Demonstrated leadership in mentoring teams and defining enterprise data and AI governance policies.