Production ML Scientist - Remote/Hybrid

Magnify; United States; Hybrid; USD 110,000 - 160,000; Full time

Job summary

Magnify is partnering with a leading organization to identify a talented Data Scientist for an exciting opportunity within their data and technology organization. This hands-on role focuses on production ML and AI, building scalable data products and deploying models in cloud environments.

You will collaborate with product managers, software engineers and stakeholders to translate business problems into data-driven solutions and integrate them into production systems using Azure, AWS, Azure ML

Qualifications

• 2–5+ years of Data Science, ML, or AI experience
• Bachelor’s degree in a technical field
• Strong Python and SQL skills
• Hands-on experience deploying ML models in production
• Experience with cloud platforms and AI/ML tooling
• Familiarity with LLMs/RAG is highly desirable
• Experience integrating models into production via APIs and data pipelines

Responsibilities

• Develop and deploy machine learning models and AI-powered data products
• Turn business challenges into scalable data science solutions
• Analyze large datasets to uncover insights and improve products
• Prepare datasets and perform feature engineering for ML
• Deploy and manage AI applications involving LLMs and RAG
• Integrate models into production via APIs and data pipelines
• Collaborate with product and engineering teams across the ML lifecycle

Skills

Python

SQL

Production ML

LLMs & RAG

Cloud platforms

Education

Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics

Tools

Azure

AWS

Azure Machine Learning

SageMaker

Job description

Magnify is partnering with a leading organization to identify a talented Data Scientist for an exciting opportunity within their data and technology organization. This hands-on role focuses on production ML and AI, building scalable data products and deploying models in cloud environments.

You will collaborate with product managers, software engineers and stakeholders to translate business problems into data-driven solutions and integrate them into production systems using Azure, AWS, Azure ML