Tech Lead - Decision Technology

📋 Description

• • Lead a team of four engineers responsible for the platform, pipelines, and production systems that support Credit Risk modelling at iwoca, ensuring reliability, observability, and reproducibility of all modelling infrastructure.
• • Set technical direction by understanding adjacent systems, workflows, and business constraints to identify and prioritize high-impact engineering problems that improve credit decision accuracy and lending volume.
• • Stay hands-on in the codebase, contributing directly to critical systems—particularly during periods of ambiguity, cross-system complexity, or when no clear owner exists—to reduce friction and strengthen platform resilience.
• • Raise engineering standards across modelling systems by enforcing rigorous practices around testing, deployment, monitoring, and documentation to ensure safe and repeatable changes in production.
• • Collaborate closely with data scientists and strategy analysts to maintain a seamless feedback loop between model development, analytical insights, and engineering implementation, ensuring models are deployable, maintainable, and interpretable.
• • Champion the use of AI tools (e.g., Cursor, Claude Code) in daily development workflows to automate boilerplate tasks, accelerate research, and free up cognitive bandwidth for complex architecture and high-stakes decision-making.
• • Mentor junior engineers, foster a culture of technical excellence, and inspire cross-functional collaboration through clear communication, principled problem-solving, and leading by example.
• • Design and evolve infrastructure that enables faster experimentation by data scientists, reducing time-to-insight and increasing the velocity of model iteration without compromising system integrity.
• • Translate business needs in credit risk into scalable engineering solutions that balance speed, accuracy, and compliance, with a deep understanding of how data quality directly impacts lending outcomes.
• • Work within a complex, data-driven domain where system failures can directly affect credit decisions, customer access to finance, and regulatory compliance—requiring meticulous attention to detail and risk-aware development practices.
• • Partner with cross-functional teams to challenge assumptions, uncover root causes of systemic issues, and co-design solutions that align engineering capability with strategic risk objectives.
• • Contribute to the design and maintenance of feature management systems, including feature stores or services that support consistent model training and inference.
• • Ensure all systems are built with reproducibility in mind, supporting auditability of model inputs, transformations, and outputs across the credit decisioning lifecycle.
• • Own the technical roadmap for the modelling platform, balancing long-term architectural improvements with urgent operational needs to maintain continuous, reliable credit decisioning.
• • Act as the primary technical interface between engineering and non-engineering stakeholders in Credit Risk, translating technical trade-offs into business language and vice versa.
• • Drive adoption of best practices in data engineering, pipeline design, and system monitoring to create a culture of ownership, transparency, and continuous improvement across the team.