Job Title: Full Stack Data Scientist (Payments & Optimization)
Location:Alpharetta, GA(Hybrid) Client: Fiserv
Employment Type: Contract
IN- Person Interview (L2)
Job Description
Role Overview
We are seeking a Full Stack Data Scientist with strong expertise in machine learning, data engineering, and payment systems to design and deliver data-driven solutions for optimizing payment routing and transaction processing.
This role requires a combination of advanced analytics, ML model development, and hands-on data engineering skills, along with domain experience in banking or financial services, and experience deploying models using cloud-native platforms such as AWS SageMaker.
Key Responsibilities
- Design, develop, and deploy machine learning models for:
- Payment routing optimization
- Transaction success rate improvement
- Payload and cost optimization
- Build, train, and deploy ML models using Amazon SageMaker
- Manage end-to-end ML lifecycle (training, tuning, deployment)
- Implement model versioning, monitoring, and retraining strategies
- Analyze large-scale transactional datasets to identify:
- Patterns, anomalies, and optimization opportunities
- Fraud signals and performance bottlenecks
- Build and maintain data pipelines ensuring:
- Data quality, integrity, and security
- Efficient data processing and transformation
- Collaborate with engineering teams to integrate ML models into production systems using APIs and microservices
- Work with real-time and batch processing systems for payment data
- Ensure compliance with financial regulations and data security standards
Required Skills & Qualifications
- 6+ years of experience in Data Science, Machine Learning, or related fields
- Strong expertise in machine learning model development and optimization techniques
- Proficiency in programming languages:
- Python (preferred)
- R or Julia (nice to have)
- Hands-on experience with ML frameworks:
- scikit-learn
- TensorFlow
- PyTorch
- Strong experience with Amazon SageMaker, including:
- Model training and hyperparameter tuning
- Deployment (endpoints, batch transform)
- SageMaker Pipelines for automation
- Model monitoring and lifecycle management
- Strong analytical skills with the ability to work on large datasets
Domain Expertise (Must Have)
- Experience working with payment systems, including:
- Credit/Debit card processing
- ACH transactions
- Electronic payments
- Understanding of:
- Payment routing logic
- Transaction lifecycle
- Authorization and settlement flows
Data Engineering Skills
- Experience building and managing:
- Data pipelines
- ETL/ELT processes
- Experience integrating SageMaker with AWS services such as:
- S3 (data storage)
- Lambda (serverless processing)
- Step Functions (workflow orchestration)
- Knowledge of:
- Data quality and validation frameworks
- Data governance and security best practices
Compliance & Domain Knowledge
- Familiarity with banking/financial services domain
- Understanding of compliance standards such as:
- PCI-DSS (Payment Card Industry Data Security Standard)
Nice to Have
- Experience with real-time streaming (Kafka, Spark Streaming)
- Knowledge of broader AWS ecosystem
- Experience with fraud detection or risk modeling
- Exposure to MLOps practices and CI/CD pipelines
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