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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying machine learning models for fraud detection, with proficiency in Python, SQL, and MLOps practices. Collaborates effectively with cross-functional teams to balance fraud prevention and user experience while ensuring model performance and continuous improvement.
Highest-signal resume keywords
Machine Learning Model DevelopmentPython ProficiencySQL ProficiencyMLOps ExperienceFraud Detection Expertise
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Build, train, and productionize ML models for fraud detection and prevention
- Develop and maintain real-time or near-real-time scoring pipelines for risk decisions
- Monitor model performance and drift in production, with continuous retraining
- Perform feature engineering on transactional and behavioral data to capture fraud signals
- Collaborate with risk, product, and engineering teams to balance fraud prevention with the experience of legitimate users
- Explore the use of generative AI as a support tool for analysis, prototyping, and documentation
- Serve as a technical reference, supporting architecture decisions and contributing to the development of more junior team members
- Work in a multidisciplinary team with ML engineers, data engineers, and Product Owners to create data-driven solutions
Requirements
What you’ll need- Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related field
- Strong experience as a Data Scientist, working independently on end-to-end ML projects from conception through production deployment
- Proficiency in Python and SQL for data analysis and modeling
- Experience with classification and/or anomaly detection, including handling imbalanced classes
- Familiarity with metrics suited to imbalanced scenarios, including precision/recall, PR-AUC, and business cost-oriented confusion matrices
- Knowledge of Machine Learning frameworks such as Scikit-Learn and XGBoost/LightGBM
- Familiarity with MLOps, model monitoring, drift detection, and continuous training
- Experience with cloud computing, preferably GCP
- Experience handling large volumes of data
- Knowledge of software development best practices, CI/CD, and version control with Git
- Strong communication and teamwork skills
- Previous experience with fraud prevention, risk, or anomaly detection in production is a plus
- Graph-based modeling for network fraud detection is a plus
- Experience with low-latency online prediction APIs is a plus
- Knowledge of model explainability techniques, such as SHAP and feature importance, is a plus
- Familiarity with generative AI/LLMs applied to productivity or analysis is a plus
- Advanced experience with MLOps on GCP, including Vertex Pipelines and Model Monitoring, is a plus
- Intermediate to advanced English proficiency, including writing, reading, and speaking, is a plus
Benefits
Comp & perks- Recruitment processes conducted 100% remotely
- An inclusive, welcoming, and diverse environment
- Opportunity to work with products and technologies that impact millions of users
- Opportunity to develop more junior team members and serve as a technical reference
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningFeature EngineeringClassificationAnomaly DetectionModel MonitoringDrift DetectionCloud ComputingData AnalysisGraph-Based ModelingLow-Latency Online Prediction APIs
Soft Skills
Strong CommunicationTeamwork
Tools & Technologies
Scikit-LearnXGBoostLightGBMGCPGit
Industry Keywords
Fraud PreventionRisk ManagementImbalanced ClassesModel ExplainabilityGenerative AI