Data Scientist / Machine Learning Engineer
Remote
Job Summary
Develop and maintain production-grade machine learning models in cloud environments, moving beyond local notebooks to scalable AWS SageMaker or equivalent platforms. Perform exploratory data analysis and design feature engineering pipelines to support robust ML workflows, while building and optimizing data pipelines that feed production systems. Monitor model performance, health, and drift post-deployment to ensure continuous reliability, and collaborate with software engineering and business stakeholders to translate goals into scalable solutions. Contribute to artificial intelligence and large language model capabilities where applicable. Requires 2+ years of professional production ML experience, strong Python and SQL skills, and availability during US Central Time business hours.
Required Qualifications
- 2+ years of professional experience building, deploying, monitoring, and maintaining production ML models in real-world environments
- Strong Python programming skills
- Advanced SQL capabilities
- Experience utilizing AWS SageMaker or equivalent enterprise ML platforms (such as Google Vertex AI or Azure ML) to support production pipelines
- Proficient with Git for version control
- A demonstrated ability to work independently within production settings
- Alignment with US Time Zones
- Availability during standard US business hours
- All interviews, documentation, and daily communication conducted exclusively in English
Desired Qualifications
- Familiarity with MLOps and orchestration tools such as MLflow, Apache Airflow, or dbt (Data Build Tool)
- Hands-on experience working with Snowflake
- Prior background in marketing, growth, experimentation, or causal inference
- Practical experience working with LLMs (Large Language Models) or autonomous AI agents
- Experience operating within Agile development environments
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