Requirements
Experience: 4+ years in Data Science with a strong emphasis on production engineering. Experience in Fintech, Lending, or Risk is highly preferred.ML Proficiency: Deep understanding of both classical machine learning (Gradient Boosting, Statistical Models) and Deep Learning frameworks.Production Engineering: Proven track record of deploying models in real-time environments. Familiarity with the concepts of Feature Stores and Model Registries is essential.Technical Stack: Expert-level Python skills. Strong proficiency in SQL and relational database design.Strategic Thinking: Ability to translate complex business KPIs (e.g., reducing Non-Performing Loans) into technical ML roadmaps.
Description
b_labs is the transformation and digitization arm of B.TECH, on a mission to change the industry as we know it. We spearhead tech innovation at B.TECH, an organization that has been a cornerstone of the Egyptian retail industry. We are the engine powering B.TECH in achieving its goal of becoming the leading omni-channel platform for consumer electronics and appliances in Egypt. By joining b labs, you will get to benefit from a collaborative startup environment, while also enjoying the stability of working within a company that has achieved strong growth year after year. You will be part of a visionary, customer-focused team with an ambitious mission--to become a trailblazer for digital retail within the Middle East.The Role We are looking for a Senior Data Scientist to lead the technical strategy for our decisioning engines. You will operate at the intersection of Data Science and Software Engineering, designing high-scale systems that power multiple business lines—from Growth & Pricing to Underwriting & Collections. You will move beyond simple model building to architecting robust, reproducible MLOps pipelines that serve real-time financial decisions.ResponsibilitiesMulti-Domain Technical Strategy: Lead the development of ML solutions across diverse contexts, ensuring that models for Credit Risk, Pricing Elasticity, and Collection Optimization utilize shared infrastructure efficiently.MLOps Architecture: Champion the adoption of modern Model Serving frameworks and Feature Stores. Design workflows that ensure feature consistency between training and real-time inference.Engineering Standards: Establish rigorous standards for Data Versioning, experiment tracking, and Hyperparameter Optimization, ensuring all research is reproducible and production-ready.Production Deployment: Oversee the transition of models from notebook environments to low-latency production APIs. Ensure models are wrapped, containerized, and integrated seamlessly with backend services.Mentorship: Guide the team in best practices for Python software engineering, including testing strategies, code structure, and performance optimization.