Our client, a cutting-edge deep-tech AI platform in the MENA region, is seeking a Senior/Principal Data Scientist [This role requires relocation to Riyadh, KSA]
As a Machine Learning Scientist, leading initiatives to merge cloud infrastructure, DevOps, and machine learning to automate and deploy advanced computational processes. This pivotal role is dedicated to developing sophisticated machine-learning models for various industries, significantly advancing our technological evolution.
You will be responsible for
- Lead initiatives to develop and implement strategies for fraud detection and AML
- Collaborate with various teams to define project roadmaps, technical and functional requirements, and deliverables.
- Conduct research, experimentation, and optimization to enhance technical solutions for detecting fraudulent activities.
- Drive the entire life cycle of fraud and AML systems, including the initial concept, implementation, and ongoing maintenance.
- Mentor and guide junior team members and support broader team initiatives, fostering a culture of continuous learning and development.
- Stay updated with industry trends, best practices, and regulatory requirements for fraud detection, AML, and financial crime prevention.
The Must-Haves
- 7+ years of experience in quantitative analytics or data modelling, with strong expertise in machine learning, predictive modelling, and algorithm development.
- 3+ years of experience in building fraud detection ML models or consulting on fraud detection/fraud prevention systems / AML
- Strong experience in Python and SQL.
- Proven experience working with cross-functional and cross-cultural teams.
- Demonstrated leadership capabilities with excellent communication skills and the ability to motivate and inspire a team.
- Strong technical skills to improve, enhance, monitor and review machine learning models
- Advanced degree in a quantitative field such as Computer Science, Statistics, Mathematics,
The Nice-to-Haves:
- Proficient in implementing graph analytics for fraud detection purposes
- Anomaly detection
- Risk scoring at onboarding, event and transaction levels
- Device intelligence applications (fingerprinting, suspicious devices, bot detection)
- Working closely with engineering in implementing ML fraud models and processes
- Working closely with the data science director and product in setting fraud strategy & roadmap.
- Comprehensive knowledge of AWS cloud services and architecture pertinent to data science projects.