Description

You will design, develop, and deploy machine learning models within an AWS environment, owning the end-to-end data science lifecycle.

This role is on-site.

Responsibilities

• Build, train, and deploy machine learning models using Amazon SageMaker and other AWS services.
• Develop and maintain data pipelines using AWS Glue, Lambda, S3, and RDS.
• Collaborate with engineering and DevOps teams to implement scalable cloud-based machine learning pipelines.
• Perform end-to-end data science tasks including data collection, preprocessing, validation, and optimization.
• Monitor model performance in production and conduct A/B testing to drive accuracy and efficiency.

Required Skills

• 8+ years of experience as a Data Scientist, preferably in a cloud environment.
• Hands-on expertise with Amazon SageMaker for model lifecycle management.
• Deep knowledge of AWS services including S3, EC2, Lambda, Glue, and Redshift.
• Proficiency in Python and machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, and Pandas.
• Strong command of SQL and experience working with large relational databases.
• Experience with R and DevOps practices for model deployment.
• Ability to perform model interpretability and tuning.

Preferred Skills

• Experience with infrastructure automation using AWS CloudFormation, AWS Step Functions, or Terraform.
• Familiarity with big data tools like Apache Spark, Hadoop, or EMR.
• Knowledge of MLOps, CI/CD pipelines, and advanced techniques like deep learning or NLP.

Key Skills

Education

Any Graduate