Description
Wise is a global technology company, building the best way to move and manage the world’s money. Min fees. Max ease. Full speed. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money.
We’re looking for a Lead Data Scientist to join our growing Scams Prevention team. This role is a unique opportunity to work behind the scenes of company transactions, understand how we mitigate risk and at the same time provide our customers with the seamless service they deserve. What you build will have a direct impact on Wise’s mission and millions of our customers.
As a Lead Data Scientist in the Scam Prevention team, you will leverage your expertise in data science to innovate and deploy models that detect and prevent fraudulent activities. Your work will directly influence our ability to safeguard our platform against unauthorised access and enhance our overall security framework. You will collaborate closely with cross-functional teams, including engineering, product, and security operations.
Key Responsibilities:
- Lead the development and deployment of advanced machine learning models to detect, predict, and mitigate account takeover attempts.
- Analyse 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.
QUALIFICATIONS:
- 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.
- A proactive, problem-solving mindset with a passion for protecting users from fraudulent activities.
- Solid knowledge of Python, and able to make and justify design decisions in your code.
- Experience with mining into event logs to identify patterns and associations
- Familiarity 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;
- Strong problem-solving skills with the ability to help refine problem statements and figure out how to solve them.