Data Scientist – Machine Learning

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, implement, and maintain machine learning models for production use.

• Conduct exploratory data analysis and feature engineering to derive actionable insights.

• Create and enhance data pipelines that facilitate ML workflows and automation.

• Work collaboratively with engineering and business partners to deliver scalable ML solutions.

• Contribute to AI/LLM-based functionalities when relevant to improve product offerings.

• Oversee and optimize model performance in production settings (not limited to notebook-based development).

• Keep thorough documentation of all processes, configurations, and modifications in a clear and auditable format.

⛳️ Requirements

• Minimum of 2 years experience in building and maintaining production machine learning models.

• Proficient in Python programming.

• Familiarity with AWS SageMaker or similar enterprise ML platforms (e.g., Vertex AI or Azure ML) that support production ML pipelines.

• Experience in deploying and monitoring ML models in a production environment (not solely notebook-based development).

• Advanced proficiency in SQL.

• Knowledge of Git/version control systems.

• Ability to operate independently in production environments.

• Experience with MLOps tools (such as MLflow, Airflow, dbt, or similar).

• Proficiency in Snowflake.

• Background in marketing, growth, experimentation, or causal inference.

• Familiarity with LLMs or AI agents.

• Understanding of agile development methodologies.

• Experience in developing scalable ML solutions within enterprise settings.

• Knowledge of cloud-based ML platforms and best practices for production deployment.

• Strong communication skills and the ability to collaborate with cross-functional teams.

• Awareness of US time zones (PST to EST) and remote collaboration processes.

🏝️ Benefits

• Flexible work arrangements

• Professional development opportunities

• Gain global exposure by collaborating on products with international teams and clients.

• Attend both virtual and in-person international technology conferences to broaden your knowledge and network.