Manager, Data Analytics
American ExpressLead analytics, performance management, and portfolio strategy for American Express commercial client management. Advise senior leaders on growth, forecasting, and data-informed decisions.
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American Express — банк ісі, қаржы және B2B төлемдері салаларында жұмыс істейтін жаһандық қаржылық қызметтер компаниясы. Компанияның қызметтері жеке тұлғаларға, бизнеске және корпоративтік клиенттерге арналған кредит карталарын, төлем және саудагерлерге қызмет көрсету сервистерін, жинақ шоттарын, депозиттік сертификаттарды және жеке қарыздарды, сондай-ақ сыйақы бағдарламаларын, саяхат бағдарламаларын, корпоративтік төлем шешімдерін, қаржылық білім беруді, қауіпсіздік қызметтерін және карта ұстаушылар мен саудагерлерге қолдау көрсетуді қамтиды.
Senior Data Science Analyst, Fraud Risk
Build machine learning and graph-based solutions to detect payment card compromise and strengthen fraud risk decisions at American Express. Analyze compromised-card data and develop scalable decisioning pipelines.
Сипаттама
- Build and improve machine learning and AI solutions that identify card compromise patterns, detect suspicious behavior, and support risk decisions.
- Use Graph Neural Networks, graph learning, Transformers, deep learning, and other advanced machine learning methods.
- Analyze large, complex, interconnected datasets to find relationships and risk signals and produce actionable business insights.
- Build and optimize analytical and modeling pipelines, from data preparation and feature engineering through training, validation, and performance monitoring.
- Use Python, PySpark, and cloud platforms such as Google Cloud to create scalable analytical solutions.
- Evaluate emerging AI and machine learning methods to improve model effectiveness, predictive accuracy, and operational efficiency.
- Work with cross-functional stakeholders to turn business challenges into data-driven solutions.
- Present analytical findings, model performance, and strategic recommendations to technical and non-technical audiences.
- Review cases in depth to improve model performance and decision-making.
- Keep analytical and modeling work aligned with business objectives and manage stakeholder expectations throughout each project.
- Review research papers and identify use cases that can advance model development and innovation.
Талаптар
- Hold a graduate or postgraduate degree in statistics, mathematics, economics, engineering, management, computer science, or a related quantitative field from a recognized institution.
- Have 2–3 years of relevant experience in data science, advanced analytics, machine learning, or predictive modeling.
- Bring strong Python and PySpark programming skills, with practical experience in data manipulation, statistical analysis, and machine learning model development.
- Understand machine learning and deep learning techniques, with exposure to Graph Neural Networks, graph algorithms, Transformer architectures, or related advanced AI methods.
- Be familiar with deep learning frameworks such as PyTorch or TensorFlow and cloud environments such as Google Cloud Platform.
- Have experience with large datasets, feature engineering, data preprocessing, and model evaluation.
- Demonstrate strong problem-solving and analytical skills, and the ability to turn complex business requirements into effective technical solutions.
- Communicate clearly in writing and speech, and present analytical insights and recommendations to varied stakeholders.
- Use sound judgment, collaborate effectively, and deliver high-quality analytical work in a dynamic business environment.
- Work the following shifts: Monday–Wednesday, 1:00 PM–9:30 PM; Thursday, 11:00 AM–7:30 PM; Friday, 8:30 AM–5:00 PM.
Артықшылықтар
- Competitive base salary.
- Bonus incentives.
- Support for financial well-being and retirement.
- Medical, dental, vision, life insurance, and disability coverage, depending on location.
- Flexible hybrid, onsite, or virtual working arrangements, depending on role and business needs.
- Paid parental leave, depending on location.
- Free access to global onsite wellness centers staffed by nurses and doctors, depending on location.
- Free, confidential counseling through the Healthy Minds program.
- Career development and training opportunities.
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