Data Science Engineer - ML

Role Overview: As a Data Science Engineer specializing in Data, your primary responsibility will be to design and implement cloud solutions, build MLOps on the cloud (preferably AWS Cloud), and construct model and data pipelines for Data Scientists and Data Engineers using AWS cloud services. You will play a crucial role in assisting with data science model review, code refactoring and optimization, containerization, deployment, versioning, and monitoring of model quality. Your hands-on experience with different features of AWS SageMaker, Python ML Libraries, and ability to communicate effectively with various teams will be key to your success in this role. Key Responsibilities: - Design and implement cloud solutions, build MLOps on the cloud. Preferably AWS Cloud. - Build model and data pipelines for Data Scientists and Data Engineers using AWS cloud services. - Assist in data science model review, code refactoring and optimization, containerization, deployment, versioning, and monitoring of model quality. - Hands-on experience with different features of AWS SageMaker including but not limited to SageMaker Studio, Jupyter Notebooks, Data Wrangler, Clarify etc. - Good understanding of the Python ML Libraries. Should be able to prototype and evaluate new libraries and new features available. - Experience in communicating with Data science team, Cloud Infrastructure team, and developers to collect requirements, describe software product features, and technical designs. - Ability and willingness to multi-task and learn new technologies quickly. - Stakeholder management with good written and verbal technical communication skills with an ability to present complex technical information in a clear and concise manner to a variety of audiences. Qualifications Required: - Bachelor's or masters degree in computer science, IT, or related technical field. - 5+ years of professional software development experience. - 3+ years of experience with programming languages such as Python, R, and open-source technologies (Apache, Hadoop, Spark, PyTorch, TensorFlow). - Proficiency in Python, R, Spark. - Machine learning knowledge and experience. - Experience building tools for data scientists and developers. Must have experience in AWS SageMaker and AWS SageMaker Studio. - Experience with IDE/notebook software (Jupyter Studio, R-Studio, VSCode, PyCharm, etc). - Experience in building data pipelines using cloud technologies (S3, Lakeformation, SQS, SNS, Kinesis, Spark, Kafka, Glue, etc). - Good to have experience in Data Visualization tools like SAS, Tableau, AWS Quicksight. Role Overview: As a Data Science Engineer specializing in Data, your primary responsibility will be to design and implement cloud solutions, build MLOps on the cloud (preferably AWS Cloud), and construct model and data pipelines for Data Scientists and Data Engineers using AWS cloud services. You will play a crucial role in assisting with data science model review, code refactoring and optimization, containerization, deployment, versioning, and monitoring of model quality. Your hands-on experience with different features of AWS SageMaker, Python ML Libraries, and ability to communicate effectively with various teams will be key to your success in this role. Key Responsibilities: - Design and implement cloud solutions, build MLOps on the cloud. Preferably AWS Cloud. - Build model and data pipelines for Data Scientists and Data Engineers using AWS cloud services. - Assist in data science model review, code refactoring and optimization, containerization, deployment, versioning, and monitoring of model quality. - Hands-on experience with different features of AWS SageMaker including but not limited to SageMaker Studio, Jupyter Notebooks, Data Wrangler, Clarify etc. - Good understanding of the Python ML Libraries. Should be able to prototype and evaluate new libraries and new features available. - Experience in communicating with Data science team, Cloud Infrastructure team, and developers to collect requirements, describe software product features, and technical designs. - Ability and willingness to multi-task and learn new technologies quickly. - Stakeholder management with good written and verbal technical communication skills with an ability to present complex technical information in a clear and concise manner to a variety of audiences. Qualifications Required: - Bachelor's or masters degree in computer science, IT, or related technical field. - 5+ years of professional software development experience. - 3+ years of experience with programming languages such as Python, R, and open-source technologies (Apache, Hadoop, Spark, PyTorch, TensorFlow). - Proficiency in Python, R, Spark. - Machine learning knowledge and experience. - Experience building tools for data scientists and developers. Must have experience in AWS SageMaker and AWS SageMaker Studio. - Experience with IDE/notebook software (Jupyter Studio, R-Studio, VSCode, PyCharm, etc). - Experience in