Data Scientist/ AWS Data Engineer
Summary
Joins a fintech data science team to develop, deploy, and monitor ML models on AWS: packaging models in SageMaker, building Lambda/API Gateway inference flows and ETL pipelines (S3, Glue, Athena), and handling logging/monitoring with CloudWatch. Needs 1+ year of hands-on AWS plus strong Python and SQL.
Role Overview
We are looking for an Data Scientist/ AWS Engineer to join India Data Science team of DolFinTech, USA. The candidate must have at least 1 year of direct hands-on experience in developing, deploying, integrating, and monitoring Machine Learning models and processes on AWS. The candidate must have hands-on experience with Amazon SageMaker, AWS Lambda, API Gateway, S3, CloudWatch, and AWS-based ETL/data pipelines.
Key Responsibilities
- Develop, package, and deploy ML models using Amazon SageMaker.
- Build and manage real-time SageMaker inference endpoints.
- Develop AWS Lambda functions for model invocation and application integration.
- Create and maintain REST/HTTP APIs using Amazon API Gateway.
- Build and maintain ETL/data-processing pipelines on AWS using services such as S3, Glue, Lambda, Athena, and/or Step Functions.
- Implement end-to-end ML scoring workflows such as:
- Application/API → API Gateway → Lambda → SageMaker → Response
- Implement logging, monitoring, and alerting using Amazon CloudWatch.
- Monitor model/API performance, latency, failures, and production issues.
- Troubleshoot AWS deployment, integration, and IAM/permission issues.
- Support model and code versioning and deployment across Development, UAT/Staging, and Production environments.
- Work with Data Scientists to convert notebook/prototype models into reliable production solutions.
Mandatory Skills
- Minimum 1 year of hands-on AWS experience
- Strong hands-on experience with Amazon SageMaker, AWS Lambda , Amazon API Gateway, Amazon S3, Amazon CloudWatch, AWS IAM, AWS ETL/data-processing pipelines, AWS Glue, Athena
- Strong Python and SQL skills.
- Experience deploying ML models into production environments.
- Experience creating and consuming REST APIs / JSON interfaces.
- Experience with Git/version control.
- Good understanding of ML models, feature engineering, model scoring, and model monitoring.
Preferred Skills
Experience with some of the following would be advantageous:
AWS Step Functions / Event Bridge
- SageMaker Pipelines / Model Registry / Model Monitor
- Docker / Amazon ECR
- CI/CD pipelines
- Terraform / CloudFormation / AWS CDK
- Fraud, credit risk, transaction risk, or financial-services models
Experience & Qualification
- 1+ years overall experience preferred
- Minimum 1 year of direct hands-on AWS experience
- Bachelor's/Master's degree in Computer Science, Engineering, Data Science, or a related discipline