About the Role As a Data Scientist at Aspire, you will be responsible for building and maintaining production machine learning solutions in a cloud environment. This role focuses on developing scalable ML models, collaborating with cross-functional teams, and supporting data-driven decision making across the organization. You will work within US time zones (PST to EST) and operate in a remote-first, distributed team environment.

What You'll Do

• Develop, deploy, and maintain production machine learning models.
• Perform exploratory data analysis and feature engineering to extract actionable insights.
• Build and improve data pipelines that support ML workflows and automation.
• Collaborate with engineering and business stakeholders to deliver scalable ML solutions.
• Contribute to AI/LLM-based capabilities where applicable to enhance product offerings.
• Monitor and optimize model performance in production environments (not only notebook-based development).
• Document all procedures, configurations, and changes in a clear, auditable manner.

Desired Candidate Profile

What You'll Need

• 2+ years of experience building and maintaining production machine learning models.
• Strong Python programming skills.
• Experience with AWS SageMaker or another enterprise ML platform (e.g., Vertex AI or Azure ML) supporting production ML pipelines.
• Experience deploying and monitoring ML models in production (not only notebook-based development).
• Advanced SQL.
• Git/version control.
• Ability to work independently in production environments.
• Experience with MLOps tools (MLflow, Airflow, dbt, or similar).
• Snowflake.
• Marketing, growth, experimentation, or causal inference experience.
• Experience with LLMs or AI agents.
• Agile development practices.
• Experience building scalable ML solutions in enterprise environments.
• Familiarity with cloud-based ML platforms and production deployment best practices.
• Strong communication skills and ability to work with cross-functional teams.
• Familiarity with US time zones (PST to EST) and remote collaboration workflows.