Lead Data Scientist - Scams Prevention

Description

You will join the growing Scams Prevention team as a Lead Data Scientist.

The role sits behind the scenes of the company's transactions. You will help manage risk while customers still get a smooth service, and your work will affect the company's mission and millions of customers.

You will use data science to build and launch models that catch fraudulent activity, guard the platform against unauthorised access and strengthen overall security. You will work with engineering, product and security operations.

The company is a global technology company that builds ways to move and manage money. It helps people and businesses send money abroad, spend overseas and make international payments, and it is building a new network for the world's money. The team is international.

Responsibilities

• Lead the building and rollout of advanced machine learning models that detect, predict and reduce account takeover attempts.
• Mine large volumes of data for trends, patterns and anomalies linked to ATO and Send Scam threats.
• Plan and run experiments that test how well fraud detection systems work, then improve them over time.
• Work with security analysts and engineers to turn business and security needs into data-driven insights and solutions.
• Build sturdy data pipelines, algorithms and tools that detect and respond to ATO and Send Scam threats in real time.
• Keep up with new developments in data science, machine learning and fraud prevention so the team's ATO and Send Scam capabilities stay current.
• Coach junior data scientists and help build a culture of teamwork and continuous learning.

Requirements

• Proven data science experience focused on fraud detection, cybersecurity or fintech.
• Experience building machine learning models for Send Scam (Victim Identification) and Account Takeover (ATO).
• Strong skills in machine learning frameworks and languages such as Python, R or similar.
• Hands-on work with large datasets and processing tools such as Hadoop, Spark and SQL.
• Knowledge of anomaly detection, supervised and unsupervised learning, deep learning and graph-based solutions.
• Ability to work with cross-functional teams and explain complex technical ideas to non-technical stakeholders.
• A proactive problem solver who cares about protecting users from fraud.
• Solid Python skills, with the ability to justify design decisions in your code. You use Git with others, including opening GitHub pull requests, and you can review code and read it, especially Java.
• Experience collaborating with engineering on services.
• Experience mining event logs to find patterns and associations.
• Familiarity with model types such as gradient boosting, neural networks, regression, autoencoders and clustering, and knowing when to use each or a blend of them.
• Experience with statistical analysis and clear presentation skills to turn insight into action.
• A strong product mindset and the ability to work independently across teams.
• Good communication skills for explaining ideas to non-technical people.
• Strong problem solving, including helping refine problem statements and work out how to solve them.
• Formal qualifications matter less than strong experience and clear reasoning.

Conditions

The company welcomes applicants from every background and especially encourages applications from under-represented groups.