Requirements

• Proven experience in a data science role focused on fraud detection, cybersecurity, or fintech domains
• Experience building 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 and unsupervised learning methods, deep learning, and graph-based solutions
• Ability to work collaboratively in cross-functional teams and communicate complex technical concepts to non-technical stakeholders
• Proactive, problem-solving mindset with a passion for protecting users from fraudulent activities
• Solid knowledge of Python and ability to make and justify design decisions in code
• Experience using Git for collaboration, including opening Pull Requests and reviewing code
• Ability to read through code, especially Java
• Demonstrable experience collaborating with engineering on services
• Experience mining event logs to identify patterns and associations
• Familiarity with a range of model types and knowledge of when to use gradient boosting, neural networks, regression, autoencoders, clustering, or blends of these
• Experience with statistical analysis and strong presentation skills to drive insight into action
• Strong product mindset and ability to work independently in a cross-functional environment
• Good communication skills and ability to convey information to non-technical individuals
• Strong problem-solving skills with ability to refine problem statements and determine solutions

What you'll be doing

• Leading development and deployment of advanced machine learning models to detect, predict, and mitigate account takeover attempts
• Analyzing large volumes of data to identify trends, patterns, and anomalies related to ATO and Send Scam threats
• Designing and implementing experiments to evaluate and improve fraud detection systems
• Collaborating with security analysts and engineers to translate business and security requirements into data insights and solutions
• Developing robust data pipelines, algorithms, and tools for real-time detection and response to threats
• Staying up-to-date on advancements in data science, machine learning, and fraud prevention techniques
• Mentoring and guiding junior data scientists and fostering a culture of collaboration and continuous learning