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

You have a solid knowledge of Python, and are able to make and justify design decisions in your code. You know how to use Git to collaborate with others (e.g. opening Pull Requests on GitHub) and are able to review code. Ability to read through code, especially Java. Demonstrable experience collaborating with engineering on services
You have experience with mining into event logs to identify patterns and associations
You are familiar 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 insight 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 get the point across to non-technical individuals
Strong problem solving skills with the ability to help refine problem statements and figure out how to solve them