Data Analyst

Comunidad de Madrid

Madrid, Spain

about 1 month ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience required

8 years minimum

Working hours

Regular working hours

Job source

www.jobleads.com

Tech stack

Data Analysis Profiling Payment Systems Fraud Prevention and Detection Python (Programming Language) Machine Learning Raw Data Backtesting Standard Sql SQL Databases Transaction Data Feature Engineering

Job description

The Risk and Fraud Operations team plays a central role in safeguarding Raisin’s business by monitoring, assessing, and mitigating risks across all operational areas. We are responsible for managing fraud prevention, detection and mitigation, investigations and recoveries, monitoring for financial crime events (AML, KYC) and implementing effective risk controls to support the company’s growth
Our work bridges business, compliance, and technology-analyzing data, processes, and transactions to identify potential threats while also enabling smooth and secure customer experiences
We collaborate closely with cross-functional teams (Product, Compliance, Customer Service, and Engineering) to design and execute risk and fraud management frameworks, enhance operational efficiency, and maintain a strong culture of accountability
As the Senior Data Analyst - Fraud Strategy & Operations, you will be the driving force behind our fraud defense system. We are looking for a proven analytical mind from the financial services sector who can challenge our current thinking, build predictive fraud models, redesign existing fraud models, framework and processes
Using SQL, Python/R Ecosystems, Feature Engineering, you will dissect fraud trends, build predictive models from scratch, and implement sharp, real-time rules to prevent and detect fraud across our payment rails
This role reports to the Head of Risk and Fraud Operations and requires a highly capable and entrepreneurial individual who can balance deep technical hands-on execution with high-level strategy
Uncover Trends: Conduct complex data analysis using SQL, Python and Feature Engineering to proactively identify emerging fraud patterns and system vulnerabilities before they impact the platform
Deploy Fraud Rules: Design, test, and implement robust fraud prevention rules that successfully catch bad actors while maintaining a seamless experience for real customers
Drive Strategy: Elevate Raisin US’s capabilities by introducing industry best practices, new methodologies, and innovative fraud prevention strategies that we aren’t using today
KPIs & Dashboards: Build out data-driven dashboards to track fraud metrics, losses, and mitigation performance, presenting actionable findings directly to leadership
Build Predictive Models: Design, build, and deploy machine learning and predictive models utilizing Python to detect anomalies across the entire customer journey (onboarding, funding, and money movement)
Feature Engineering: Develop model features based on identity, device, behavioral, and transactional data
Cross-Functional Delivery: Partner closely with Product and Engineering to integrate these models into our real-time production pipelines
Risk Profiling: Partner with the Compliance team to enhance customer risk profiling, transaction monitoring, and KYC/AML workflows
Design low-friction, custom risk rules for identity verification, account takeover protection, and transaction monitoring
Continuous Back-Testing: Routinely stress-test current rules against changing regulatory standards and evolving financial crime tactics

Requirements

Subsidised Urban Sports Club Membership- Financial Services Background: 8+ years of experience in fraud risk management, analytics, or financial crime specifically within fintech, retail banking, or digital payments
Master of Analytics: Exceptional analytical capabilities are your biggest asset. You love diving into raw data to solve complex puzzles
Rule & Model Builder: Proven track record of designing custom fraud rules and deploying machine learning or statistical models in a live environment
Technical Stack: Highly proficient in SQL and Python for data manipulation, analytics, and building predictive models. Experience building out fraud dashboards is a must
Payment System Domain Expertise: Deep understanding of Deposits and ACH is required; direct experience with modern instant payment systems like RTP and FedNow is highly preferred