Customer Intelligence & Hyper-Personalization - Lead

PLEASE NOTE THAT THIS ENGAGMENT IS FOR AN ONSITE ROLE IN RIYADH ON A CONTRACT BASIS FOR 3-6 MONTHS.WORK MODE: Onsite - RiyadhENGAMENT TYPE & DURATION: Contract | 3-6 Months (Likelihood of extension) EXPERIENCE: 12+ YOEJOB DESCRIPTION:This is not a traditional banking analytics role. We are specifically looking for practitioners who have built and scaled 1:1 personalization engine at consumer internet or e-commerce platforms and who can transplant that thinking into financial services.Responsibilities: Design and architect a hyper-personalization engine that prices and packages retail banking products (personal finance, credit cards, wealth management) at the individual customer level — not the segment level.Build a unified customer understanding layer that synthesizes income, existing product relationships, transaction behaviour, and lifecycle signals into actionable individual profiles.Define the decision logic and ML models that determine what offer, at what price, through which channel, at what moment for each customer independently.Translate e-commerce personalization patterns (recommendation engines, dynamic pricing, real-time offer decisioning) into the retail banking context.Work with product, engineering, and data teams to operationalize personalization at scale across digital and branch channels.Establish measurement frameworks: uplift testing, A/B experimentation, and continuous feedback loops to prove incremental value per customer.Who You Are:Required: 5–12+ years of hands-on experience building personalization, recommendation, or dynamic pricing systems at a major e-commerce or consumer internet platform (Amazon, Temu, Shopee, Flipkart, Alibaba, Grab, or comparable).Deep understanding of individual-level customer modelling — you think in terms of individual propensity, not segment averages.Track record of shipping production systems that tailor offers, pricing, or product bundles in real-time based on customer signals.trong technical foundation in ML/data science (Python, feature engineering, model deployment) combined with product thinking.Experience with experimentation frameworks (A/B testing, multi-armed bandits, uplift modelling) at scale.Preferred: Prior exposure to financial services, fintech, or payments - but this is secondary to e-commerce personalization depth.Experience with real-time decisioning infrastructure (feature stores, streaming pipelines, low-latency serving).Familiarity with GCC or emerging market consumer dynamics.Experience working in consulting-adjacent or client-embedded delivery models.Client’s Vision:Building a next-generation customer intelligence capability for a leading retail bank. The mandate is clear: move from broad segment-based approaches to true individual-level personalization where every customer receives a uniquely tailored proposition based on their full profile, relationship depth, and behavioral signals.

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