Overview
Remote
On Site
Full Time
Part Time
Accepts corp to corp applications
Contract - W2
Contract - Independent
Skills
Video
IDEA
Performance Monitoring
IT Management
Collaboration
Mentorship
Research
Presentations
Risk Management
Python
Algorithms
Credit Risk
Management
Fraud
Use Cases
Debugging
Git
Docker
Linux
Google Cloud
Google Cloud Platform
Data Flow
Apache Beam
Continuous Integration
Continuous Delivery
Cloud Computing
Machine Learning (ML)
SANS
Natural Language Processing
Job Details
Job Title: Senior Machine Learning Engineer Risk & Fraud Detection
Location: Remote
Job Type: Contract
Interview: Video
Job Description
We are seeking a Senior Machine Learning Engineer with a strong background in risk decisioning and fraud detection. The ideal candidate is passionate about applying advanced ML techniques to solve real-world problems at scale, such as detecting transactional fraud, account takeovers, fake accounts, and other abuse scenarios. This role involves both technical leadership and hands-on engineering, working on end-to-end ML systems in a production environment.
Key Responsibilities
- Develop and deploy scalable ML solutions for fraud and risk detection (e.g., anomaly detection, graph ML, deep neural networks)
- Build and improve ML systems and data architectures for large-scale processing
- Own the entire ML lifecycle from idea generation to model development, deployment, and performance monitoring
- Provide strategic technical leadership in ML design and architecture
- Collaborate with engineering and cross-functional teams on solution delivery
- Mentor junior team members and contribute to team growth
- Contribute to the ML community through research papers or conference presentations (e.g., KDD, ICML, NeurIPS)
Required Skills
- 3+ years of experience building and deploying ML models in fraud/risk management
- Proficiency in Python and strong knowledge of ML algorithms
- Experience with Airflow and building end-to-end ML pipelines
- Familiarity with credit risk management and fraud detection use cases
- Hands-on experience with model deployment, debugging, and tuning in large-scale environments
- Strong foundation in working with git, Docker, and Linux-based systems
Preferred Skills
- Experience with Google Cloud Platform (Google Cloud Platform), BigQuery, and Dataflow (Apache Beam)
- Exposure to infrastructure-as-code and CI/CD practices in cloud ML environments
- Background in graph neural networks, NLP, or reinforcement learning is a plus
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.