Data Science Lead — Credit Risk & AI Automation

About Blnkblnk is a fintech company on a mission to enable financial inclusion through point-of-sale financing. Less than 4% of Egyptians have access to credit cards. Many consumers rely on cash savings or are forced to borrow from informal lenders at high interest rates to purchase products and services. We’re changing this by enabling consumers to access credit within minutes at their preferred merchants.About The Roleblnk is looking for a Data Science Lead to drive the development and improvement of machine learning solutions for credit risk assessment and consumer lending. The role combines strong data science expertise, software engineering, ML systems design, and technical leadership, with a focus on using LLMs and AI agents to automate data science workflows.The successful candidate will lead two ML engineers, own the data science roadmap, and ensure that ML solutions are accurate, scalable, maintainable, and deliver measurable business value.Key Responsibilities Credit Risk Modeling: Lead the development and optimization of ML models for credit scoring, underwriting, repayment prediction, risk segmentation, and customer behavior analysis. Data Science & Experimentation: Drive feature engineering, exploratory data analysis, statistical analysis, label design, and rigorous model evaluation. AI-Assisted Automation: Apply LLMs, RAG systems, and AI agents to automate data analysis, experimentation, model evaluation, and reporting. Technical Leadership: Lead and mentor two ML engineers, define technical direction, review implementations, and establish standards for code quality, testing, reproducibility, and documentation. Software Engineering & System Design: Guide the design of robust ML systems, including data pipelines, model-serving architectures, APIs, integrations, scalability, reliability, and maintainability. Apply sound software engineering principles and make informed architectural trade-offs. Production ML: Oversee model deployment, versioning, monitoring, and validation, including model performance degradation and data drift. Cross-Functional Collaboration: Work with risk, product, and business teams to translate business objectives into measurable ML solutions. Required QualificationsStrong experience in applied data science and machine learning, preferably in fintech, consumer lending, or banking.Solid understanding of statistical modeling, probability, feature engineering, experimental design, and model validation.Strong Python and SQL skills, along with familiarity with common ML libraries.Strong software engineering fundamentals, including modular design, testing, debugging, code reviews, and maintainable code.Understanding of system architecture and the full ML lifecycle, from data pipelines and training to deployment, integration, monitoring, and production operations.Experience taking ML models from experimentation to production, with an understanding of scalability, reliability, performance, and operational trade-offs.Demonstrated technical leadership, project ownership, and experience mentoring engineers.Practical knowledge of LLMs and their application to data science automation.Preferred QualificationsExperience in credit scoring, probability-of-default estimation, risk segmentation, or alternative-data modeling.Familiarity with model explainability, calibration, temporal validation, class imbalance, and risk-model monitoring.Experience building LLM-powered analytical workflows, RAG systems, or AI agents.Experience with automated training pipelines, experiment tracking, and ML lifecycle management.Success CriteriaImproved predictive performance and reliability of credit risk models.Faster, more reproducible data science experimentation.Well-designed, maintainable production ML systems.Effective technical leadership and development of the ML team.Reduced manual effort through reliable AI-assisted automation.