SeniorFull TimeEN
Founded in 2011, Fasanara is a global asset manager and technology platform managing c. USD 5.7 billion AUM (as of December 31, 2025) in Fintech strategies on behalf of pension funds and insurance companies in Europe and North America. With c.130 employees globally, we are a pioneer investor in Fintech Lending and Digital Asset investing.
Fasanara manages the largest and longest-standing Fintech Lending fund in Europe and invests in early-stage Fintech companies via its venture capital vehicles, using its central role in the Fintech ecosystem to identify and back revolutionary new businesses.
Our Culture
We are strong believers in meritocracy, and we seek to reward people based on impact, judgement, and excellence in execution. We keep bureaucracy to a minimum, so decisions are made quickly and people have real ownership. Our environment is collaborative, inclusive, entrepreneurial, and built on trust.
We set ambitious goals, work hard in a focused and sustainable way, and place a strong emphasis on teamwork, integrity, and quality in everything we do. We know we are only as good as our people, so we are deliberately building the firm around exceptional talent and diverse perspectives, and we support our leaders to grow, influence, and shape the future of the business.
The Role
The Risk Analytics Lead, VP is responsible for leading the development and delivery of portfolio risk analytics across Fasanara's Asset-Backed Finance (ABF) strategies. This role sits at the intersection of Risk, Investment, and Technology, translating complex portfolio data into actionable insights to support investment decisions, portfolio construction, and risk monitoring.
The position combines strong technical expertise in data analytics and modelling with a commercial mindset, supporting multiple stakeholders including Portfolio Management, Capital Markets, and Investor Relations. The role also involves managing and developing junior team members, driving the evolution of the analytics platform, and ensuring high standards of data quality, governance, and scalability.
Responsibilities
Portfolio Risk Analytics & Insights
• Lead the execution of portfolio risk analytics across ABF strategies, including concentration, exposure, performance diagnostics, and outlier investigations.
• Deliver clear, decision-ready insights to support the Risk and Investment teams.
• Monitor portfolio risk and limits, identifying emerging risks and escalating issues where appropriate.
• Develop originator-level analytics and monitoring frameworks, including performance tracking and early risk identification.
Analytics Frameworks & Model Development
• Translate business and risk requirements into scalable analytics frameworks, including metric definitions, monitoring tools, and reporting outputs.
• Drive the development, enhancement, and validation of risk models, ensuring robustness and alignment with portfolio characteristics.
• Enhance scenario analysis and stress testing capabilities across portfolios under different market conditions.
• Ensure full documentation, reproducibility, and auditability of all models and analytics.
Data & Platform Development
• Work closely with Tech and Data Engineering teams to improve the analytics platform, focusing on data quality, reconciliation, and scalability.
• Design and implement analytics using granular (loan-level) data across multiple originators and products.
• Standardise and automate analytics and reporting outputs to improve efficiency and consistency.
• Structure and harmonise heterogeneous datasets into consistent portfolio-level views.
Stakeholder Collaboration
• Partner with Portfolio Management and Capital Markets teams to support portfolio construction, origination assessment, and strategic initiatives.
• Provide analytics and documentation for regulatory, audit, and governance processes.
• Support broader business functions including Investor Relations with data-driven insights.
• Communicate analytical findings clearly to both technical and non-technical stakeholders.
Team Leadership & Execution
• Coordinate and oversee the day-to-day activities of the Risk Analytics team, setting priorities and ensuring timely delivery.
• Review team outputs to ensure quality, consistency, and accuracy.
• Mentor and develop junior team members, supporting their technical and professional growth.
• Manage multiple workstreams simultaneously, maintaining a strong ownership and execution mindset.
Requirements
Essential
• 5+ years of experience in data analytics within private credit, asset-backed finance, securitisation, or related fields.
• Strong understanding of ABF / securitisation fundamentals, including collateral analysis, portfolio monitoring, and structure mechanics (e.g. waterfalls, triggers).
• Advanced Python skills for data analysis, modelling, and production-level code.
• Advanced SQL skills for working with large and complex datasets.
• Strong experience designing and implementing portfolio analytics using granular data.
• Proven ability to analyse concentration, exposure dynamics, and performance trends across portfolios.
• Experience working with multiple datasets from different sources, including data standardisation and reconciliation.
• Solid understanding of risk modelling techniques, including implementation, testing, and documentation.
• Strong grasp of portfolio risk concepts and early warning indicators.
• Experience coordinating or mentoring junior team members.
• Ability to translate business and risk requirements into analytical outputs.
• Strong communication skills, with the ability to present insights in a structured and actionable way.
• Experience working cross-functionally with Risk, Investment, and Technology teams.
Desirable
• Experience in structuring or exposure to securitisation transactions.
• Familiarity with scenario analysis and stress testing frameworks.
• Experience with Git and collaborative development environments.
• Understanding of data engineering concepts (data pipelines, ingestion, transformation).
• Exposure to fintech lending platforms or non-bank lending environments.
• Interest in leveraging AI tools and technologies to enhance analytics capabilities.
Benefits
