We are looking for a data-driven fraud expert to join our Identity & Risk Analytics team. This role blends advanced analytics, statistical modeling, and fraud strategy to drive measurable impact across fraud prevention initiatives. You’ll work with large-scale datasets, build predictive models, and evaluate the performance of fraud mitigation tools—all while collaborating with cross-functional teams and external vendors.
This is a high-impact opportunity for a Data Scientist who thrives in complex environments and enjoys translating data into strategic decisions.
responsibilities :
Analyze fraud trends across First Party, Third Party, Account Takeover, and Account Origination scenarios using statistical and machine learning techniques.
Build predictive models and cost-benefit frameworks to assess fraud mitigation tools and strategies.
Conduct post-implementation analytics to validate the effectiveness of fraud controls.
Collaborate with vendor management teams to assess tool performance, reconcile invoices, and forecast budget impacts.
Prepare executive-level dashboards and reports to support strategic decision-making.
Ensure model governance and compliance with internal and regulatory standards.
Use advanced data tools (Python, R, SAS, SQL) to conduct exploratory analysis and synthesize insights from multiple sources.
requirements-expected :
Bachelor’s or Master’s degree in Data Science, Statistics, Finance, Economics, or related field.
5+ years of experience in fraud analytics, financial crime modeling, or data science roles.
Strong command of statistical modeling, forecasting, and cost-benefit analysis.
Proficiency in Python and SQL (required); SAS and R (preferred).
Experience working with cross-functional teams and external vendors.
Excellent communication skills with the ability to present complex findings clearly.
Ability to work independently in a fast-paced, ambiguous environment.
offered :
Hybrid schedule with an in-office expectation of 3+ days/week.
Working hours: 14:00–22:00 (flexibility available).
