We are looking for an Assistant Manager (Data Scientist) who can independently execute high‑quality Data Analytics, Data Science, and Engineering work within the AML domain. The role involves designing and developing machine learning, deep learning, graph analytics, and LLM/RAG‑based solutions to enhance AML detection, behavioral insights, and investigative intelligence. You will transform complex datasets into clear, actionable insights, and contribute to building production‑ready analytical assets that support AML operational and business decisions.
You will work across AML, TM, Investigations, Technology, Cloud/Data Engineering, and Compliance teams, engaging with structured, semi‑structured, unstructured, and graph data in cloud‑native environments. The role requires strong analytical thinking, hands‑on modelling expertise, solid data engineering fundamentals, and adherence to MRM, CBUAE expectations, data governance, and internal standards.
If you are passionate about applying advanced ML/GenAI techniques in a real‑world financial‑crime context and want to grow into a full‑stack data science leader, this role is an excellent opportunity.
The leading financial institution in MENA
While more than half a century old, we proudly think like a challenger, startup, and innovator in banking and finance, powered by a diverse and dynamic team who put customers first. Together, we pioneer key innovations and developments in banking and financial services. Our mandate? To help customers find their way to Rise Every Day, partnering with them through the highs and lows to help them reach their goals and unlock their unique vision of success. Delivering superior service to clients by leading with innovation, treating colleagues with dignity and fairness while pursuing opportunities that grow shareholders value. We actively contribute to the community through responsible banking in our mission to inspire more people to Rise.Responsibilities:
- Lead and execute data-driven initiatives within the AML domain, working independently on projects with minimal guidance.
- Design and develop advanced machine learning, deep learning, and graph analytics models to strengthen AML detection and behavioral intelligence.
- Build and deploy LLM/RAG-based analytical tools, investigative copilots, and knowledge retrieval systems to enhance investigative capabilities.
- Perform in-depth behavioral analytics, network analysis, and typology detection to support AML operations and decision-making.
- Create reusable analytical assets and insights for AML stakeholders, utilizing dashboards and structured summaries for effective communication.
- Collaborate with engineering teams to implement reliable ML/LLM pipelines, ensuring seamless CI/CD workflows and monitoring.
- Integrate analytical components with downstream systems through APIs and microservice interfaces for efficient data flow.
- Apply robust data governance principles, ensuring data quality, metadata management, and access controls across enterprise-wide data.
- Prepare and maintain MRM-compliant documentation, validation packs, and monitoring frameworks to ensure model compliance and effectiveness.
Qualifications:
- A bachelor's degree in Computer Science, Data Science, or a related field is required.
- A minimum of 5 years of experience in a similar role, with a strong background in AML compliance and data analytics.
- Proven expertise in developing advanced machine learning, deep learning, and graph analytics models for AML use cases.
- Experience with LLM/RAG technologies and their application in investigative and knowledge-retrieval systems is essential.
- In-depth knowledge of AML regulations, including CBUAE guidelines and internal governance frameworks.
- Excellent collaboration and communication skills, with the ability to work effectively across diverse teams.
- Proficiency in data engineering and management, including data structures, feature stores, and analytical datasets.
- Familiarity with cloud-native platforms and their role in handling enterprise-wide structured, semi-structured, and unstructured data.
- Analytical mindset with a problem-solving approach, and a passion for driving data-driven initiatives and innovation.
- Ability to work independently, manage projects, and ensure compliance with regulatory standards and expectations.
