Responsibilities

• Analyze large, complex datasets to uncover trends, patterns, and insights. Perform exploratory data analysis (EDA) leveraging AWS data processing tools.
• Design, build, train, and evaluate machine learning models using AWS SageMaker and frameworks such as TensorFlow.
• Utilize AWS Glue, Redshift, Textract, and other data engineering tools to preprocess, clean, and transform data for machine learning workflows.
• Develop end-to-end, automated machine learning pipelines on AWS for seamless deployment and operationalization of models at scale.
• Work closely with data engineers, business analysts, and stakeholders to understand business requirements and customize data science solutions accordingly.
• Deploy models into production environments and implement monitoring systems to track model performance, accuracy, and reliability using SageMaker and AWS Lambda.
• Maintain clear documentation of models, methodologies, and results. Communicate findings effectively to stakeholders to support data-driven decisions.

Qualifications

• Proven experience in data analysis, machine learning model development, and deployment.
• Strong proficiency with AWS services including SageMaker, Glue, Redshift, Textract, Lambda, and associated data processing tools.
• Hands-on experience with machine learning frameworks such as TensorFlow.
• Solid understanding of ETL processes and data pipeline development.
• Excellent communication skills to collaborate with cross-functional teams and present findings.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field preferred.

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