2 of 2 Data Science Jobs in Knutsford

Architect to shape the technology strategy and architecture that underpins creditworthiness assessment and lending decisions. This role is critical to developing intelligent, automated credit decisioning platforms that bring together data, analytics, regulatory compliance, and enterprise architecture. You will design the overall lending decision architecture, working closely with risk teams, data scientists, and senior stakeholders to build systems that … efficient, fair, and compliant lending outcomes. Key Responsibilities . Architect end-to-end credit risk decisioning and affordability systems . Design and implement decision engines, integrating internal and external data sources . Translate credit policy and business objectives into technical requirements and rule frameworks . Partner with data science teams to integrate statistical and machine learning models … a subject-matter expert across risk, technology, and senior leadership teams Required Skills & Experience . Deep expertise in decision engine platforms - eg FICO Blaze, Pega, or similar . Strong data analytics and programming capability (Python, R, SAS) . Proficient in enterprise system integration and data warehousing environments . Understanding of cloud platforms (AWS, Azure, GCP) for scalable decisioning More ❯

the technology strategy and systems used to assess the creditworthiness and financial capacity of loan applicants. This role focuses on building the overall architecture for the lending process, combining data, advanced analytics, and regulatory knowledge to create automated, efficient, and compliant decision-making platforms. Responsibilities Architecting decision platforms: Design, build, and implement the technical architecture for end-to-end … credit risk decisioning and affordability solutions. This includes integrating various data sources (internal and external), analytical models, and rule-based engines. Developing credit strategy: Translate business objectives and credit policy into technical requirements and system rules. This includes defining risk appetite, decision flows, and the criteria for approving, denying, or referring credit applications. Overseeing model development: Work closely with … data scientists and credit risk model developers to integrate statistical and machine learning models into the decisioning architecture. This includes models for credit scoring, loss forecasting, and affordability assessments. Ensuring regulatory compliance: Incorporate all relevant regulatory requirements (eg, responsible lending obligations) into the decisioning framework. The architect must ensure the system provides a robust audit trail for all lending More ❯