Data Scientist

Leading Global Bank is seeking a hands-on Data Scientist to design, build, and deploy scalable machine learning and advanced analytics solutions across the bank’s high-impact domains - including Next Best Action (NBA), fraud detection, payments optimization, cross-border transaction intelligence, customer growth analytics, and risk-aligned decisioning.

This role is ideal for a practitioner who combines deep technical expertise with strong business intuition, and who has a proven track record delivering production-grade models that improve fraud prevention, accelerate payments, enhance customer engagement, and drive measurable revenue and operational outcomes.

The Data Scientist will work closely with business, engineering, product, risk, compliance, and operations teams to operationalize analytics and AI across critical banking workflows.

Responsibilities

• Develop and deploy machine learning models for fraud detection, payments intelligence, cross-border transaction scoring, NBA personalization, customer segmentation, churn prediction, and growth analytics.
• Own end-to-end model delivery: business problem framing, data engineering, feature engineering, model development, validation, deployment, monitoring, and continuous improvement.
• Build scalable ML pipelines using Azure ML, Databricks, Spark, and cloud-native data platforms.
• Operationalize models into production workflows through APIs, microservices, and real-time decision systems.
• Design NBA strategies that optimize customer engagement, product adoption, and lifetime value across retail and commercial banking.
• Develop fraud and payments models that reduce loss, improve authorization rates, and enhance cross-border transaction safety.
• Implement robust model governance, explainability, monitoring, recalibration, and compliance controls aligned with banking and regulatory expectations.
• Establish best practices for MLOps, CI/CD automation, experiment tracking, and production monitoring.
• Collaborate with risk, compliance, legal, audit, and technology teams to ensure responsible AI adoption.
• Translate complex analytical findings into clear, actionable insights for senior leaders.
• Perform other duties as assigned.

• 10+ years of hands-on experience in data science, machine learning, quantitative modeling, or advanced analytics within financial services, fintech, payments, or other regulated industries.
• Proven experience delivering production-grade ML models with measurable business impact.
• Deep expertise in Python, SQL, statistical modeling, predictive analytics, and distributed data processing.
• Strong practical experience with Azure ML, Databricks, Spark, TensorFlow, PyTorch, scikit-learn, XGBoost, MLflow, and cloud-native ML ecosystems.
• Experience implementing MLOps frameworks, including CI/CD, model deployment, monitoring, and governance.
• Strong understanding of model risk management, explainability, auditability, data governance, privacy, and regulatory expectations.
• Hands-on experience integrating ML solutions into enterprise applications, APIs, and operational workflows.
• Strong process orientation with the ability to redesign workflows using data-driven insights and AI-enabled automation.
• Demonstrated ability to influence senior stakeholders across business, risk, technology, and operations.
• Excellent communication and executive presentation skills.
• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or related quantitative discipline.

Highly Preferred

• Direct experience in commercial banking, consumer banking, payments, fraud, AML/BSA, cross-border transactions, or regulatory reporting environments.
• Experience deploying Generative AI, LLMs, NLP, or intelligent automation use cases (NBA, Next Best Action, Banker Copilot, workflow automation).
• Knowledge of SR 11-7, CCAR, CECL, BCBS 239, and enterprise governance frameworks.
• Experience designing feature stores, vector retrieval systems, real-time inference architectures, or streaming ML pipelines.
• Experience leading AI initiatives from proof-of-concept through scaled production adoption.
• Master’s degree or PhD in a quantitative discipline.
• Demonstrated ability to build and scale high-performing analytics teams.