Machine Learning Engineer R&D, Fraud Intelligence

Working at the intersection of applied research and large-scale engineering, you'll develop novel approaches in anomaly detection, supervised learning, and continual learning to uncover and adapt to evolving fraud patterns. Assist in the development and optimization of machine learning models. Preprocess and analyze datasets to ensure data quality. Collaborate with senior engineers and data scientists on model deployment. Conduct experiments and run machine learning tests. Stay updated with the latest advancements in machine learning. Model Development: Design and implement core decision models for identity, onboarding, authentication, abuse, scam, product-specific models. Anomaly Detection: Develop and refine algorithms for detecting anomalies and identifying potential fraud patterns. Supervised Learning: Apply supervised learning techniques to build predictive models that accurately identify fraudulent activities. Continuous Learning: Utilize continual learning methods to continuously improve model performance and adapt to new fraud tactics. Collaboration: Work closely with cross-functional teams, including tech, operations, and product teams, to integrate fraud prediction models into various systems and processes. Experimentation and Analysis: Conduct experiments, analyze results, and interpret findings to drive innovation and enhance decision-making processes. Data Integrity: Ensure data integrity and consistency by working closely with business stakeholders and engineers to address critical data challenges. Advocacy: Promote and maintain a data-driven culture by engaging with diverse internal teams and advocating for best practices in data science and fraud prevention. Minimum of 2 years of relevant work experience and a Bachelor's degree or equivalent experience. Familiarity with ML frameworks like TensorFlow or scikit-learn. Strong analytical and problem-solving skills. Master's degree or PhD in Computer Science, Statistics, Data Science, Machine Learning, Artificial Intelligence, or a related quantitative field (STEM). 3+ years of experience within ML Engineering or AI Research roles, with demonstrated expertise in building and deploying real-world predictive models. Domain Knowledge: Experience in fraud prevention and detection. Expertise: Strong understanding of anomaly detection, supervised learning techniques, and experiential learning methods. Familiarity with decision models for identity and authentication. Instrumentation: Experience driving data instrumentation for experimentation and large-scale data collection. Real-time Systems: Familiarity with building systems that incorporate real-time feedback and continuous learning. Advanced Techniques: Knowledge of reinforcement learning, contextual bandits, sequence models, optimization, or graph mining. Strong interpersonal, written, and verbal communication skills, with experience collaborating across multiple business functions.