Senior Data Analyst - Fraud Strategy & Operations

Raisin•Lehi, UT

4d•Hybrid

About The Position

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.

Requirements

8+ years of experience in fraud risk management, analytics, or financial crime specifically within fintech, retail banking, or digital payments.
Exceptional analytical capabilities are your biggest asset. You love diving into raw data to solve complex puzzles.
Highly proficient in SQL and Python for data manipulation, analytics, and building predictive models.
Experience building out fraud dashboards is a must.
Deep understanding of Deposits and ACH is required.
Proven track record of designing custom fraud rules and deploying machine learning or statistical models in a live environment.

Nice To Haves

Direct experience with modern instant payment systems like RTP and FedNow is highly preferred.

Responsibilities

Conduct complex data analysis using SQL, Python and Feature Engineering to proactively identify emerging fraud patterns and system vulnerabilities before they impact the platform.
Design, test, and implement robust fraud prevention rules that successfully catch bad actors while maintaining a seamless experience for real customers.
Elevate Raisin US’s capabilities by introducing industry best practices, new methodologies, and innovative fraud prevention strategies that we aren't using today.
Build out data-driven dashboards to track fraud metrics, losses, and mitigation performance, presenting actionable findings directly to leadership.
Design, build, and deploy machine learning and predictive models utilizing Python to detect anomalies across the entire customer journey (onboarding, funding, and money movement).
Develop model features based on identity, device, behavioral, and transactional data.
Partner closely with Product and Engineering to integrate these models into our real-time production pipelines.
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.
Routinely stress-test current rules against changing regulatory standards and evolving financial crime tactics.