Description

You will operationalize complex machine learning models into production through end-to-end deployment and scaling.

Responsibilities

• Deploy and scale models and algorithms in collaboration with data science and engineering teams.
• Design, develop, and maintain adaptable data pipelines for use-case specific data.
• Integrate ML use cases into business pipelines, ensuring smooth handshakes with upstream and downstream teams.
• Develop and maintain pipelines to generate and publish model performance metrics for model review cadences.
• Support operationalized models and create runbooks for ongoing maintenance.

Required Skills

• 10+ years of experience in machine learning or related engineering roles.
• Experience operationalizing complex ML models into production environments.
• Strong understanding of standard ML algorithms including Regression and Classification.
• Knowledge of Natural Language Processing concepts such as sentiment generation, topic modeling, and TFIDF.
• Proficiency with scikit-learn, vader sentiment, pandas, and PySpark.
• Ability to design and maintain end-to-end data pipelines.
• Experience developing performance metric reporting for model risk oversight.
• Bachelor's degree or equivalent graduate-level education.

Key Skills

Education

Any Gradute