We have an exciting and rewarding opportunity for you to take your career to the next level.
As an Analytics Solution Manger in Consumer and Community Banking , you will drive delivery of analytics data products and applied AI/ML solutions for Consumer & Community Banking . You will own end-to-end execution-from problem framing and requirements through engineering, model development, productionization, and operational excellence-while partnering across Product, Technology, Data Governance, and Risk/Controls.
Job Responsibilities
- Partner with stakeholders to translate business needs into clear problem statements, KPIs, and success metrics (e.g., servicing insights, customer engagement, marketing measurement, forecasting, anomaly detection).
- Own delivery plans, dependencies, RAID management, and status communications.
- Design and build scalable, well-controlled pipelines (batch and, where needed, streaming) to produce trusted curated datasets.
- Develop analytics-ready layers (data marts/semantic views) with consistent metric definitions and strong documentation (data dictionary, lineage, runbooks).
- Implement data quality controls (tests, reconciliation, timeliness/completeness checks) and observability (monitoring/alerting, SLAs/SLOs).
- Build and deploy ML solutions (e.g., propensity/segmentation, next-best-action components, time-series forecasting, anomaly detection, NLP for servicing/contact-center insights).
- Own feature engineering, model selection, validation, and evaluation ensure robustness against leakage and appropriate handling of bias/fairness considerations.
- Establish MLOps practices: reproducibility, model/version governance, automated testing, drift/performance monitoring, retraining triggers, and rollback strategies.
- Ensure solutions meet security and data governance expectations (least-privilege access, sensitive data handling, retention, auditability).
- Partner with control functions to produce required artifacts (documentation, traceability, operational procedures) and support reviews/audits.
- Apply disciplined SDLC practices: code reviews, CI/CD, environment promotion, and incident/root-cause management. Optimize performance and cost across data processing, storage, and model serving.
Required Qualifications, Capabilities, and Skills
- Significant experience delivering enterprise data and analytics solutions in complex environments.
- Advanced SQL and strong data modeling skills strong Python (or equivalent) for data and ML development.
- Hands-on experience with modern data platforms (warehouse/lakehouse) and orchestration frameworks.
- Proven applied AI/ML experience across the lifecycle (framing → build → deploy → monitor), including appropriate evaluation and monitoring.
- Strong controls mindset: comfort operating with governance, change management, access controls, and audit/evidence expectations.
- Strong written and verbal communication ability to influence without authority across cross-functional teams.
Preferred Qualifications, Capabilities, and Skills
- Experience applying AI/ML in retail banking/consumer domains (marketing, servicing, digital, customer insights in partnership with risk/fraud where relevant).
- MLOps tooling experience (model registry/experiment tracking, automated pipelines, monitoring).
- NLP/GenAI experience for internal productivity or servicing insights (with appropriate governance).
- Streaming/CDC/event-driven patterns supporting near-real-time analytics or detection.
