Requirements
Requires 8+ years of experience as a Data Scientist or ML Engineer with a proven track record of deploying models into production. Must have a B.Sc. or Master's degree in Computer Science, Machine Learning, AI, or Statistics. Expertise in Python and ML frameworks like Scikit-Learn, TensorFlow, and PyTorch is essential, along with a strong understanding of probability, linear algebra, and optimization metrics. Experience with NLP for Arabic and computer vision for ID verification is needed, as is the ability to present complex algorithmic logic to non-technical stakeholders.
Description
Who we are- onebank was established in 2020 as the company responsible for launching the 1st digital native bank in Egypt. The digital bank aims to create innovative solutions tailored to serve the needs of the banking customers in Egypt.-Our main goal is to create a positive customer experience through the differentiated journey that our customers live while using the digital bank.-Our Drive: We use our drive and commitment to energies, engage and inspire others, upholding the highest standards of work ethic, honesty and morality.Role Purpose:Build and refine the core ML algorithms driving intelligence across the bank including fraud prevention engines, eKYC document verification intelligence, and behavioral cross-sell algorithms.Responsible For:Descriptive, Predictive, and Prescriptive AI, Recommendation Engines, GenAIRequirements (Qualification & Skills):Years of Experience: 8+ years of practical experience as a Data Scientist or ML Engineer, with a track record of taking complex models into production.Education: B.Sc. or Master's degree in Computer Science, Machine Learning, AI, or Statistics. Certifications in deep learning or frameworks like TensorFlow/PyTorch are strongly preferred.Expert in Python and frameworks such as Scikit-Learn, XGBoost, TensorFlow, PyTorch, and Hugging Face. Knowledge of natural language processing (NLP) for Arabic customer interactions and computer vision for ID verification.Exceptional grasp of probability, linear algebra, loss optimization, and evaluation metrics (AUC-ROC, F1-Score) under highly imbalanced data contexts (such as fraud datasets).Excellent problem-solving agility, mentorship capabilities for junior data scientists, and clear presentation of algorithmic logic to non-technical partners.Extensive experience building, validating, and optimizing real-time transactional classification models (such as financial fraud detection or AML monitoring).Preferred: Native understanding of localized Egyptian Arabic NLP and dialect variations for conversational and text analytics.