Description

Validate, monitor, and challenge machine learning models to ensure they meet business, technical, and regulatory standards.

This role is on-site.

Responsibilities

• Lead model monitoring by tracking performance metrics, detecting data and model drift, and identifying data quality issues.
• Conduct rigorous model validation through performance testing, benchmarking, and providing remediation plans during development phases.
• Perform root cause analysis on model performance degradation and recommend strategies for remediation.
• Explore and aggregate terabyte-scale datasets to uncover anomalies impacting algorithm performance.
• Partner with Product and Engineering teams to solve complex problems and explain results to non-technical audiences.

Required Skills

• 2+ years of experience in data science, engineering, or mathematics.
• Proficiency in Python or R for developing data science pipelines and workflows.
• Experience writing and tuning SQL queries.
• Hands-on experience handling terabyte-sized datasets using Spark or PySpark.
• Knowledge of machine learning techniques and key parameters affecting model performance.
• Experience using ML libraries such as scikit-learn.
• Ability to write production-level, well-documented, and explainable code.
• Skills in data visualization to communicate findings effectively.

Key Skills

Education

Any Graduate