Fraud Specialist

albo

2d

About The Position

albo albo is a leading fintech company offering financial products to individuals and SMBs with the mission to bring financial freedom to everyone everywhere. About the role In this role, you will serve as the strategic engine driving our fraud defense. You are tasked with dissecting intricate fraudulent activities and identifying latent risks to reinforce the data architecture supporting our AI and machine learning systems. By uniting high-level investigative research with technical implementation, you will collaborate with Data and ML Engineering to safeguard the integrity of our financial platform. What you’ll be doing at albo Forensic and Emergency Analysis: Execute deep exploratory and forensic analysis on complex, unstructured fraud incidents that fall outside the scope of our automated AI agents due to a lack of structured data. Engineering Support & Feature Engineering: Analyze vast sets of historical data using BigQuery to discover, validate, and propose new predictive variables (features). You will provide the critical insights needed for our ML & AI Engineers to incorporate these features into AI agents and the overarching rules engine. Strategic Research (Roadmap): Lead high-priority, critical analysis projects defined in our strategic roadmap. This includes characterizing suspicious user communities, mapping AML (Anti-Money Laundering) networks, and structuring the methodological distinction between credit default (mora) and actual fraud. Statistical Modeling & Prototyping: Design and develop prototypes for statistical models and analytical scripts (e.g., graph and connection analysis for money laundering detection) to validate logic before the AI Engineering team automates them in production.

Requirements

• Bachelor degree in Accounting or Finance (Degrees in Data Science, Actuarial Science, or Mathematics are also highly valued).
• Advanced SQL & BigQuery Skills: Strong proficiency in generating complex queries for data extraction, manipulation, and analysis within BigQuery.
• Data Prototyping & Statistical Knowledge: Experience prototyping statistical models and using scripting languages (such as Python or R) for exploratory data analysis, graph analysis, and feature validation.
• Fraud Domain Expertise: Deep understanding of fintech fraud typologies (ATO, onboarding fraud, synthetic identities) and the ability to translate suspicious payment patterns into technical rules.
• Analytical Problem-Solving: A highly analytical mindset with the ability to bring structure to unstructured data, specifically when investigating complex anomalies or evaluating the rentability vs. risk of new financial products.
• Cross-functional Collaboration: Excellent communication skills to effectively translate forensic findings into actionable technical requirements for Data Scientists, ML Engineers, and operational teams.
• Customer Centricity: You're customer-obsessed, constantly thinking about improving the customer experience.
• Hands-on: We’re looking for a comfortable and willing leader to be a hands-on contributor who is energized by rolling up their sleeves, making things happen, and enabling the team to do the same.
• Done is better than perfect: As things change and move quickly, you are excited by the fast pace and opportunity to constantly learn and help your team learn.
• Communication: Excellent ability to communicate internally and externally, quickly build relationships, and work cross-functionally.
• Extreme ownership: You hold yourself accountable to a high bar. You are supremely organized and see what needs to happen to achieve goals.
• Results & Data Driven: You understand the business metrics you are responsible for, and you demonstrate these insights to drive constant improvement.

Responsibilities

• Execute deep exploratory and forensic analysis on complex, unstructured fraud incidents that fall outside the scope of our automated AI agents due to a lack of structured data.
• Analyze vast sets of historical data using BigQuery to discover, validate, and propose new predictive variables (features).
• Provide critical insights needed for our ML & AI Engineers to incorporate these features into AI agents and the overarching rules engine.
• Lead high-priority, critical analysis projects defined in our strategic roadmap.
• Characterize suspicious user communities, map AML (Anti-Money Laundering) networks, and structure the methodological distinction between credit default (mora) and actual fraud.
• Design and develop prototypes for statistical models and analytical scripts (e.g., graph and connection analysis for money laundering detection) to validate logic before the AI Engineering team automates them in production.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level