About the Company:

Ouro is a global, vertically-integrated financial services and technology company dedicated to the delivery of innovative financial empowerment solutions to consumers worldwide. Ouro’s financial products and services span prepaid, debit, cross-border payments, and loyalty solutions for consumers and enterprise partners.

Ouro's flagship product Netspend provides prepaid and debit account solutions that connect customers with secure, convenient access to global payment networks so they can manage their money and make everyday purchases. With a nationwide U.S. retail network, customers can purchase and reload Netspend products at 130,000 reload points and over 100,000 distributing locations.

Since Ouro's founding in 1999 by industry pioneers Roy and Bertrand Sosa, Ouro products have processed billions of dollars in transaction volume and served millions of customers worldwide. The company is headquartered in Austin, Texas with regional offices around the world. Learn more at www.ouro.com.

About the Role

The Risk Data Science team is looking for a Risk Data Scientist to help the team with building, deploying, monitoring, and iterating ML models. The role will have regular opportunities for building visualizations and monitoring of our AI/ML models, track our KPI’s, work on data analysis to find opportunities to reduce friction on our products, deliver more value to our customers by identifying risk strategy opportunities, and automate internal processes within the risk and ops teams. This role will report directly to the VP of Risk Data Science.

Responsibilities

Find, process, cleanse, and verify the integrity of data used for analysis and statistical models. 

Build and select features, build and optimize classifiers for risk and automation using machine learning techniques.

Create automated anomaly or risk detection systems.

Build and publish dashboards for ongoing monitoring of strategy performance. Prepare clear and concise reports and presentations summarizing findings, insights, and recommendations.

Collaborate closely with coworkers in the Risk Data Science, Risk, and Ops teams, and enable more robust QA processes.

Build and optimize queries, determine best sources for data and insights.

Identify product opportunities, design experiments to test hypotheses, report on learnings, and be an SME on implementation of impactful insights.

Assist in determining, scoping, and creating data products and data governance related to the Risk Data Science team.

Engage on both strategic ad-hoc requests and projects, conducting independent research to propose rules and strategies to help mitigate risks across all products.

Proactively stay updated on industry trends, best practices, and regulatory requirements related to risk management and fraud prevention.

Requirements

Must be curious and passionate about problem-solving.

Excellent understanding of machine learning techniques and algorithms, such as decision trees, random forest, xgboost, etc.

Experience with common data science toolkits, such as Python (preferred) or R. Excellence in at least one of these is highly desirable.

Great communication skills.

Experience with building data visualizations and dashboards (Qlik preferred, or Tableau, Looker, Power BI, etc.).

Proficiency in using SQL for data analysis. (We will look for a SQL proficiency level of comfortably using JOIN and GROUP BY statements.)

Strong analytical skills with the ability to work with large datasets to identify patterns, trends, and anomalies in time series data.

BS or advanced degree in STEM or related field.

2+ years’ experience in data science or related roles, preferably in the Fintech or Payments industry.

Familiarity with card payments or banking/finance industry, including knowledge of transactional payment data analysis, experience with card association Risk and Dispute process/procedures is preferred.

Must be able to clearly communicate with technical and non-technical audiences.

Self-starter and effectively self-manage pivoting on priorities when needed. 

Must be a team player and regularly collaborate with team members to enhance the culture of helpfulness and high impact.