Lead Data Scientist - Trust and Safety

Requirements:

• Proven experience in a data science role with a focus on fraud detection, cybersecurity, or fin tech related domains
• Have built machine learning models for Send Scam (Victim Identification) and Account Takeover (ATO)
• Strong proficiency in machine learning frameworks and programming languages such as Python, R, or similar
• Experience working with large datasets and data processing technologies (e.g., Hadoop, Spark, SQL)
• Familiarity with anomaly detection, supervised, unsupervised learning methods, deep learning, and graph-based solutions
• Demonstrated ability to work collaboratively in cross-functional teams and effectively communicate complex technical concepts to non-technical stakeholders

What you'll be doing:

• Lead the development and deployment of advanced machine learning models to detect, predict, and mitigate account takeover attempts
• Analyze large volumes of data to identify trends, patterns, and anomalies associated with potential ATO and Send Scam threats
• Design and implement experiments to evaluate the effectiveness of fraud detection systems and continuously improve their performance
• Collaborate with security analysts and engineers to translate business and security requirements into actionable data insights and solutions
• Develop robust data pipelines, algorithms, and tools to support real-time detection and response to ATO and Send Scam threats
• Stay informed about the latest advancements in data science, machine learning, and fraud prevention techniques to ensure state-of-the-art capabilities in ATO and Send Scam
• Mentor and guide junior data scientists, fostering a culture of collaboration and continuous learning within the team

Nice to have:

• You have a solid knowledge of Python and are able to make and justify design decisions in your code
• Experience with mining event logs to identify patterns and associations
• Experience with a range of model types, and know when and why to use gradient boosting, neural networks, regression, autoencoders, clustering or a blend of these
• Experience with statistical analysis and good presentation skills to drive insights into action
• A strong product mindset with the ability to work independently in a cross-functional and cross-team environment
• Good communication skills and ability to convey technical information to non-technical individuals
• Strong problem-solving skills with the ability to help refine problem statements and figure out how to solve them