Conduct a thorough analysis of data to identify patterns, trends, and anomalies that indicate potential risks or fraudulent behavior.
Develop and implement machine learning models and algorithms to detect and prevent fraudulent activities, abuse, and security threats.
Utilize Azure Machine Learning services and other relevant technologies to build scalable and reliable machine learning workflows.
Documenting the systems you help build.
Encouraging the technical growth of your peers.
Collaborate closely with cross-functional teams including data scientists, software engineers, product managers and content moderators to integrate machine learning solutions into production systems.
Evaluate and improve existing machine learning models and algorithms based on performance metrics and feedback from operational deployments.
Identifying the vulnerabilities in products that lead to abuse.
Reviewing new products and providing consultation to product teams.
Stay updated with the latest advancements in machine learning, fraud detection techniques, and security protocols to continuously enhance our capabilities.
Requirements & Skills:
2+ years of experience operationalizing machine learning and data science.
1+ year experience in any of these technologies: GraphQL, Flink, Python, SQL, Azure Machine Learning, Kusto.
1+ years experience in Data Visualization, Data Storytelling, or other strong written and verbal communication skills.
Experience in Trust and Safety, National Security, or fighting spam, malware, fraud, and threat actor activity at scale.
Experience in responsible AI.
Experience in Safety-by-Design.
Strong understanding of machine learning algorithms (supervised and unsupervised learning, anomaly detection, etc.) and their practical implementation.
Excellent problem-solving skills and the ability to translate business requirements into technical solutions.
Experience in deploying machine learning models in production environments.
