Data Scientist

1 week ago

Alpharetta, GA, United States E-Solutions Full-time

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Location: Alpharetta, GA who can work onsite from Day 1 (5 days)

Duration: Longterm Contract

Skills Required:

4-8 years’ experience of applied machine learning/ML Ops in BFS / Investment Management industry
PhD or MS in Computer Science, Statistics or related field
Expertise in Machine Learning algorithms and frameworks:
- Training and tuning pre-trained models
- Working with structured and unstructured for Fraud models
Deep proficiency in Python with experience developing production-quality Python modules
Strong domain focus on fine-tuning and enhancing fraud detection models
Deploying models in AWS production environments
Strong command on AWS cloud stack with working knowledge of architecture components i.e., SageMaker, Bedrock, Lambda, Lex, CloudWatch, CloudTrail, Redshift ML, DynamoDB, CodeBuild, CodeDeploy, S3, EC2, IAM, AMIs
Proficient in API development using Fast API, Flask, etc. delivering asynchronous AI inference services and scalable API solutions for AI-powered applications.
Good command over statistical principles of data and model quality e.g., PSI, model performance metrics etc.

Roles and Responsibilities:

Work closely with Onsite Lead, Data scientists, Data Engineers, and QA and client stakeholders.
Evaluate input data for various statistical properties i.e., data drift using PSI and other metrics
Develop methods for monitoring data and models and efficient processes for updating or replacing old models with ones trained on new data or with the latest, state-of-the-art, pretrained models available
Skilled in evaluation metrics like precision, recall, F1-score, and AUC-ROC, ensuring high accuracy and precision in classification and regression models for Fraud.
Ensure right-fitting of architecture in AWS for the models at hand to optimize model inferencing
Strong working command of AWS SageMaker, MLFlow, and CloudWatch is a must
Should have hands on experience with deploying CI/CD Pipelines in AWS
Assist with documentation and governance of all ML and NLP pipeline artifacts
Find innovative solutions that increase automation and simplify work in AI workflows
Refactor and productionize research code, models and data while maintaining the highest levels of deployment practices including technical design, solution development, systems configuration, test documentation/execution, issue identification and resolution.