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Lead Fraud Data Scientist
Felix
WorldwideRemote4w ago
- Employment
- Full-time
- Seniority
- Lead
About the role
- Technical Leadership & Strategy: Define the long-term machine learning strategy for the fraud team, establish technical best practices, and mentor junior data scientists.
- End-to-End Model Development: Own the entire lifecycle of fraud detection models, from data exploration and feature engineering to model training, validation, deployment, and monitoring.
- Credit & Lending Fraud Mitigation: Design and develop models specifically targeted at lending fraud typologies, including synthetic identity fraud, first-party loan default fraud, and application fraud.
- Advanced Analysis: Conduct deep-dive investigations into emerging fraud patterns and user behavior, using clustering, outlier detection, network analysis, and other unsupervised techniques to uncover hidden risks and organized fraud rings.
- Experimentation: Design and execute A/B tests to measure the impact of new models, rules, and strategies on both fraud detection rates and user experience.
- Stakeholder Collaboration: Partner closely with Product, Engineering, Risk, and Operations teams to translate business needs into data science solutions, seamlessly integrate ML scores with rule engines, and communicate complex results to non-technical audiences.
- Productionalize Models: Deploy, monitor, and maintain machine learning models in a cloud environment, ensuring high availability and performance.
- Reporting & Visualization: Build and maintain dashboards using tools like Tableau or Looker to track key performance indicators (KPIs) like fraud loss rates, false positive rates, and model performance.
- Experience: 5+ years of experience in a hands-on data science role, building and deploying machine learning models.
- Leadership: Proven experience leading complex data science projects from inception to production, including setting technical direction and guiding peers.
- Python: Expert-level Python for data analysis and modeling (pandas, scikit-learn, etc.).
- SQL: Advanced SQL skills for complex data extraction and manipulation.
- Machine Learning Modeling: Deep experience with tree-based ML models (XGBoost, CatBoost, LightGBM) and statistical models (Logistic Regression, Lasso/Ridge).
- Model Explainability & Ethics: Deep understanding of model explainability frameworks (SHAP, LIME) and algorithmic fairness to ensure models comply with credit lending regulations.
- Sampling Techniques: Strong understanding of sampling techniques for handling highly imbalanced datasets.
- Unsupervised Learning: Practical experience with clustering and outlier detection techniques (e.g., K-Means, K Nearest Neighbors, Isolation Forest).
- Model Lifecycle & Cloud: Proven experience with the full modeling lifecycle, including model deployment, monitoring, and maintenance on a cloud platform like GCP, AWS, or Azure.
- Analytical Rigor: A solid foundation in statistics and experience designing and analyzing A/B tests.
- Communication: Excellent stakeholder management and communication skills, with a demonstrated ability to explain complex technical concepts to diverse audiences. Advanced English level.
- Domain Experience: Direct experience in a FinTech, payments, or risk/fraud-focused role, particularly with exposure to credit or consumer lending.
- Alternative & Bureau Data: Experience working with traditional credit bureau data (Experian, Equifax, TransUnion) and alternative credit/identity data sources.
- Graph ML: Experience with Graph Neural Networks (GNNs) or graph analytics tools (e.g., Neo4j, NetworkX) to map complex fraud networks.
- Regulatory Familiarity: Familiarity with consumer lending regulations (e.g., FCRA, ECOA) and their impact on machine learning model development.
- MLOps: Hands-on MLOps experience (e.g., CI/CD for models, versioning, automated retraining).
- GCP / Vertex AI: Experience with Google Cloud Platform (GCP), especially Vertex AI.
- Spanish and/or Portuguese speaker
- Competitive salary
- Initial stock options grant
- Annual performance bonus
- Health, dental, and vision plans
- Remote work environment, although we have offices in Miami and México City and would love to work in hybrid model if you are up to it.
- Continuous learning opportunities
- Unlimited PTO
- Paid parental leave
- Empowering opportunities for growth in a dynamic entrepreneurial environment
Perks & benefits
- Vision Insurance
- Unlimited Vacation
- Paid Time Off
- Equity Compensation
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