Description

You will operationalize complex machine learning models into production, owning the end-to-end deployment lifecycle. You design, develop, and maintain adaptable data pipelines and integrate ML use cases into business workflows. You support operationalized models by developing runbooks and generating model performance metrics for risk oversight.

Responsibilities

• Deploy and scale models and algorithms in collaboration with data science and engineering teams.
• Design and maintain data pipelines to support use-case specific data requirements.
• Integrate ML use cases into business pipelines, ensuring smooth information flow with upstream and downstream teams.
• Develop and publish model performance metrics for model review and risk oversight.
• Create and maintain runbooks for the operational support and maintenance of deployed models.

Required Skills

• 10+ years of professional software or data engineering experience.
• Strong working knowledge of Python ML packages including scikit-learn, pandas, and PySpark.
• Understanding of Natural Language Processing concepts such as sentiment analysis, topic modeling, and TFIDF.
• Experience with standard machine learning algorithms including regression and classification.
• Ability to work with tools like vader sentiment for text analysis.
• Proven track record of operationalizing ML models into production environments.

Key Skills

Education

Any Gradute