PROJECT MANAGER BANKING DATA & AI

📣 إعلان

منذ 19 ساعة تقريباً

	نوع العقد	دوام كامل
	طبيعة الوظيفة	بالموقع
	الموقع	الرياض

وصف الوظيفة

About the Role

Duncan & Ross is seeking a Project Manager - Banking - Data & AI to lead a large-scale Data Lake Modernization program for a banking client in Riyadh. This full-time role involves managing the technical migration and modernization of data platforms, alongside implementing AI use cases such as hyper-personalization of banking products and Next Best Offer (NBO) capabilities. The Project Manager will bridge technical teams, business stakeholders, and senior banking client sponsors, requiring expertise in both traditional data/infrastructure delivery and AI/ML-enabled business transformation.

Key Responsibilities

Own end-to-end project planning, governance, and delivery of the data lake modernization program, including scope, timeline, budget, resourcing, and risk/issue management.
Lead delivery of the AI/ML use case workstream for hyper-personalization and Next Best Offer, coordinating data scientists, ML engineers, and banking product/marketing stakeholders.
Define and manage the project roadmap covering data platform migration (legacy to modern data lake/lakehouse architecture), data pipeline modernization, data governance, and AI model deployment.
Act as the primary point of contact for senior client stakeholders (CIO/CDO/Head of Marketing/Retail Banking), managing expectations, steering committees, and executive reporting.
Coordinate cross-functional teams including Data Engineering, Data Science/AI, Cloud/Infrastructure, Business Analysts, and Change Management resources.
Ensure alignment between the data lake modernization workstream and AI use case delivery, sequencing dependencies correctly (*, data readiness before model deployment).
Manage third-party vendors/technology partners (*, cloud providers, data platform vendors) as required for the project.
Drive adoption of Agile/hybrid delivery methodologies, managing sprints, backlogs, and release planning for both data engineering and AI workstreams.
Ensure robust data governance, data quality, and regulatory compliance (SAMA, data privacy/PDPL) are embedded throughout the modernization effort.
Track and report on business value realization from AI use cases (*, uplift in offer acceptance rates, personalization engagement metrics, campaign ROI).
Identify, escalate, and mitigate project risks, particularly around data migration integrity, model performance, and change adoption.
Support change management and business readiness activities to ensure banking product and marketing teams can effectively operationalize NBO/hyper-personalization outputs.
Prepare and present steering committee packs, status reports, and business case tracking for senior leadership and client sponsors.

Qualifications and Experience

Bachelor's degree in Computer Science, Data/Information Systems, Engineering, Business, or a related field. A Master's degree (MBA or technical) is an advantage.
Project management certification such as PMP, PRINCE2, or equivalent is required.
Agile certification (*, CSM, SAFe) is highly desirable given the hybrid delivery approach.
Minimum of 7+ years of project/program management experience, including at least 3+ years managing large-scale data platform/data lake or data engineering programs.
Prior experience delivering AI/ML-enabled use cases in a banking or financial services context; experience with personalization engines, recommendation systems, or Next Best Offer/Next Best Action is strongly preferred.
Experience working within a consulting environment, managing client relationships, steering committees, and multi-vendor delivery teams.
Experience in the banking/financial services sector in Saudi Arabia or the broader GCC is strongly preferred; familiarity with SAMA regulatory and data governance requirements is a plus.
Proven track record managing cross-functional teams spanning data engineering, cloud infrastructure, data science, and business/marketing stakeholders.
Experience with cloud data platforms (*, AWS, Azure, GCP, Snowflake, Databricks) and modern data architecture concepts (data lakehouse, data mesh) is highly valued.