Accountabilities:

Conduct quantitative analysis and model tuning across AML, sanctions, and fraud detection use cases.

Develop, validate, and optimize statistical and machine learning models to improve detection accuracy and overall performance.

Build and evaluate classification models using logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest algorithms.

Apply appropriate model tuning methodologies, optimization techniques, and analytical tools to identify opportunities for improvement.

Use Python and SQL for data analysis, feature engineering, statistical modeling, and process automation.

Analyze large and complex datasets to identify data quality issues, modeling gaps, trends, and opportunities for enhancement.

Compare model performance using relevant statistical measures and recommend strategies to improve effectiveness.

Document model development and tuning methodologies, analytical results, performance metrics, and optimization activities.

Prepare clear reports and present findings to senior management, regulatory stakeholders, and cross-functional teams.

Collaborate with business, technology, compliance, and model governance teams to resolve analytical and modeling challenges.

Maintain high standards of code quality, documentation, and version control using tools such as Git and GitHub.

Requirements

Master’s degree or Ph.D. in Statistics, Economics, Finance, Mathematics, Data Science, or another relevant quantitative discipline.

Professional experience in banking or financial services, preferably involving AML, sanctions, or fraud modeling.

Strong understanding of financial crime risks, banking products, processes, and systems.

Hands-on experience developing, validating, and evaluating statistical and machine learning classification models.

Practical knowledge of logistic regression, multinomial logit, XGBoost, LightGBM, and Random Forest.

Advanced proficiency in Python and SQL, including the ability to write production-quality code for data analysis, feature engineering, and statistical modeling.

Strong knowledge of model tuning methodologies, optimization techniques, statistical concepts, and model performance evaluation.

Experience processing and analyzing large datasets and implementing automation solutions.

Familiarity with version control tools such as Git and GitHub.

Strong quantitative reasoning, analytical thinking, and problem-solving capabilities.

Ability to investigate and resolve data quality issues and modeling challenges.

Excellent technical documentation and reporting skills, including the ability to explain model tuning outcomes to senior management and regulators.

Strong written and verbal communication skills, with the ability to present complex analytical findings clearly.

Ability to collaborate effectively with interdisciplinary teams and manage multiple priorities in a changing environment.

Benefits

Competitive compensation; specific salary range not disclosed.

Opportunity to work on complex financial crime analytics and modeling initiatives.

Exposure to banking, sanctions, fraud prevention, and AML technology projects.

Collaborative, international, and fast-growing professional environment.

Opportunities for career development and continuous learning.

A culture that values innovation, teamwork, and professional growth.

Specific healthcare, retirement, paid leave, and other benefits are not detailed in the posting.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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