Staff Machine Learning Engineer

PayPal-posted 2 months ago
$169,500 - $291,500/Yr
Full-time • Senior
Hybrid • Chicago, IL
5,001-10,000 employees
Credit Intermediation and Related Activities
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We are seeking a talented Senior Machine Learning Engineer to join our team and focus on building advanced fraud prediction models. This role involves developing core decision models for various aspects of fraud prevention, including identity, onboarding, authentication, abuse, scam and product-specific models. The ideal candidate will leverage anomaly detection, supervised learning, and experiential learning techniques to create robust and effective fraud prevention solutions.

  • Design and implement core decision models for identity, onboarding, authentication, abuse, scam, product-specific models.
  • Develop and refine algorithms for detecting anomalies and identifying potential fraud patterns.
  • Apply supervised learning techniques to build predictive models that accurately identify fraudulent activities.
  • Utilize continual learning methods to continuously improve model performance and adapt to new fraud tactics.
  • Work closely with cross-functional teams, including tech, operations, and product teams, to integrate fraud prediction models into various systems and processes.
  • Conduct experiments, analyze results, and interpret findings to drive innovation and enhance decision-making processes.
  • Ensure data integrity and consistency by working closely with business stakeholders and engineers to address critical data challenges.
  • Promote and maintain a data-driven culture by engaging with diverse internal teams and advocating for best practices in data science and fraud prevention.
  • Master's degree or PhD in Computer Science, Statistics, Data Science, Machine Learning, Artificial Intelligence, or a related quantitative field (STEM).
  • 5+ years of experience within ML Engineering or AI Research roles, with demonstrated expertise in building and deploying real-world predictive models.
  • Strong understanding of anomaly detection, supervised learning techniques, and experiential learning methods. Experience in fraud prevention is a plus.
  • Strong interpersonal, written, and verbal communication skills, with experience collaborating across multiple business functions.
  • Familiarity with decision models for identity and authentication.
  • Experience in fraud prevention and detection.
  • Experience driving data instrumentation for experimentation and large-scale data collection.
  • Familiarity with building systems that incorporate real-time feedback and continuous learning.
  • Knowledge of reinforcement learning, contextual bandits, sequence models, optimization, or graph mining.
  • Flexible work environment
  • Employee shares options
  • Health and life insurance
  • Annual performance bonus
  • Equity
  • Medical, dental, vision, and other benefits
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