Data / ML Engineer – Scientist

Roles and Responsibilities

• Build and maintain reusable Python modules for ML model performance evaluation and monitoring.
• Develop logic for generating executive summaries, insights, and recommendation outputs for model monitoring reports.
• Design and implement modular frameworks for automated report generation in PDF and dashboard formats.
• Standardize model monitoring templates including visualizations, commentary, KPI thresholds, and performance metrics.
• Develop automation and orchestration workflows to schedule recurring monitoring runs and archive outputs.
• Process and analyze large-scale model outputs using Databricks, PySpark, and parquet datasets.
• Collaborate with data scientists, ML engineers, and business stakeholders to enhance model governance and monitoring capabilities.
• Support ML lifecycle management, model registry integration, and production monitoring processes.
• Ensure code quality, scalability, maintainability, and adherence to engineering best practices.

Required Qualifications

• 7–10 years of hands-on experience in Python development for Data Science and Machine Learning applications.
• Strong expertise in Python libraries including pandas, NumPy, scikit-learn, matplotlib, and seaborn.
• Strong understanding of ML evaluation metrics for classification and regression models.
• Experience with Databricks Notebooks and PySpark for large-scale data processing.
• Experience handling parquet data formats and large model output datasets.
• Experience with PDF generation libraries and automated reporting frameworks.
• Knowledge of MLFlow, model registries, and ML production monitoring platforms.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience with workflow orchestration tools such as Airflow or similar platforms.
• Familiarity with enterprise ML governance and monitoring frameworks.
• Experience building scalable and reusable analytics/reporting frameworks.