In this role, you will analyze large and/or complex datasets, develop predictive models, and derive actionable insights that drive key business decisions.
We are seeking a highly analytical and detail-oriented Data Scientist with experience in Risk and Fraud analytics to join our growing team. This role will focus on developing and deploying machine learning models, statistical methods, and data-driven strategies to detect risky behaviors and prevent fraudulent activities across our products and services.
Key Responsibilities
- Collect, clean, and analyze large, complex datasets from multiple sources.
- Develop predictive models and machine learning algorithms to support decision-making and improve business performance.
- Translate business problems into data-driven solutions with measurable impact.
- Develop and deploy machine learning models to detect, predict, and prevent fraudulent transactions and behavior patterns.
- Analyze large volumes of structured and unstructured data from multiple sources to identify fraud trends and root causes.
- Collaborate with fraud operations, engineering, and compliance teams to implement real-time fraud detection solutions.
- Design and monitor KPIs to evaluate model performance and improve fraud detection systems over time.
- Conduct deep-dive investigations into fraud cases, creating detailed reports and actionable insights.
- Stay current with emerging fraud techniques, industry best practices, and data science tools.
Required Qualifications
- Bachelor’s or master’s degree in data science, Computer Science, Statistics, Mathematics, Economics or a related field.
- 10+ years of professional experience in data science
- Proficient in Python, SQL, SAS and machine learning techniques
- Experience in responsible use of AI if used in solution design
- Strong analytical skills and the ability to identify patterns and trends from data
- Experience working with large datasets and cloud platforms (e.g., AWS, GCP, Azure).
- Strong understanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis.
- Experience with visualization tools like Tableau and Power BI.
Skill Matrix:
- Familiarity with graph analytics or network-based fraud detection tools. Highly desired
- Knowledge of regulatory frameworks and compliance issues related to fraud and financial crime. Highly desired
- Strong communication skills with the ability to explain technical solutions to non-technical stakeholders. Highly desired
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Economics or a related field. Required
- Professional experience in data science. Required 10 Years
- Proficient in Python, SQL, SAS and machine learning techniques. Required 5 Years
- Experience working with large datasets and cloud platforms (e.g., AWS, GCP, Azure). Required 5 Years
- Understanding of supervised and unsupervised fraud detection techniques, including anomaly detection, behavioral modeling, and network analysis. Required
- Experience with visualization tools like Tableau and Power BI. Required 5 Years
- Experience in responsible use of AI if used in solution design. Required 5 Years
- Strong analytical skills and the ability to identify patterns and trends from data. Required
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