
Staff Machine Learning Engineer
- Chicago, IL
- Permanent
- Full-time
- 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.
- Education: Master's degree or PhD in Computer Science, Statistics, Data Science, Machine Learning, Artificial Intelligence, or a related quantitative field (STEM).
- Experience: 5+ years of experience within Data Science, ML Engineering, or AI Research roles, with demonstrated expertise in building and deploying real-world predictive models.
- Skills: Strong understanding of anomaly detection, supervised learning techniques, and experiential learning methods. Experience in fraud prevention is a plus.
- Communication: Strong interpersonal, written, and verbal communication skills, with experience collaborating across multiple business functions.
- Expertise: Familiarity with decision models for identity and authentication.
- Domain Knowledge: Experience in fraud prevention and detection.
- 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.