Staff Data Scientist at mylo

Cairo, Cairo, Egypt -

Full Time

Start Date

Immediate

Expiry Date

27 Apr, 26

Salary

0.0

Posted On

27 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, MLOps, Python, SQL, Machine Learning, Deep Learning, Production Engineering, Model Serving, Feature Stores, Hyperparameter Optimization, Credit Risk, Pricing Elasticity, Collection Optimization, Experiment Tracking, Data Versioning, Technical Strategy

Industry

Financial Services

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. The Role We are looking for a Staff 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. 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: 7+ 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.

Responsibilities

Lead the technical strategy for decisioning engines and develop ML solutions across diverse contexts. Oversee the transition of models to production and ensure best practices in Python software engineering.