Description

You will build, deploy, and manage machine learning models within an AWS ecosystem.

Responsibilities

• Design and deploy machine learning models using Amazon SageMaker and other AWS services.
• Develop and maintain data pipelines utilizing AWS Glue, Lambda, S3, and RDS.
• Collaborate with engineering and DevOps teams to implement scalable cloud-based ML pipelines.
• Conduct A/B testing and deploy models into production environments.
• Monitor model performance and optimize for accuracy and efficiency.

Required Skills

• 8 years of experience as a Data Scientist, preferably in cloud environments.
• Hands-on expertise with Amazon SageMaker for model building, training, and deployment.
• Deep proficiency in Python and machine learning libraries including TensorFlow, PyTorch, Scikit-learn, and Pandas.
• Strong knowledge of AWS services: S3, EC2, Lambda, Glue, and Redshift.
• Advanced SQL skills for working with large relational databases.
• Experience with R and DevOps practices for model deployment strategies.
• Proven ability to perform end-to-end data science tasks from preprocessing to optimization.
• Competency in model interpretability and tuning.

Preferred Skills

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

Key Skills

Education

Any Graduate