mylo
Fintech
SeniorDataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Engineer at mylo. Skills: ML solutions, MLOps Architecture, Production deployment. Lead ML solutions development. Design MLOps workflows”
Industry & Context.
What They're Looking For.
Must Have
4+ years in Data Science, Expert-level Python skills, Proficiency in SQL
Nice to Have
Experience in Fintech, Experience in Lending, Experience in Risk
What You'll Do.
Lead ML solutions development
Design MLOps workflows
Establish Data Versioning standards
Optimize Hyperparameter
Oversee model transition
Integrate models with services
Guide team on Python best practices
Full Job Description
**mylo** is a fintech platform dedicated to helping millions of people and businesses thrive by providing accessible and responsible financial solutions. Whether you’re purchasing a mobile phone, a new jacket, a flight ticket, a comfy couch, or even covering school tuition, mylo enables you to buy now and pay later at thousands of points of sale across Egypt. Born out of B.TECH—Egypt’s leading electronics and appliances retailer with over 27 years of experience in offering buy now, pay later solutions—mylo brings a legacy of trust and innovation to the fintech space. All mylo products are fully Sharia-compliant, ensuring ethical and inclusive financial practices. We are seeking a passionate and experienced **Senior Data Engineer** to join our team within the Fintech domain. This role is ideal for someone who thrives in a fast-paced environment and is excited to design, build, and scale secure, high-performing infrastructure to support a range of financial products. Responsibilities * Multi-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. **Requirements** * **Experience:**
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