Risk Analytics Associate
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What you'll do
- Contribute to the development of portfolio risk analytics across ABF strategies, including: concentration, exposure, performance tracking, stress testing, outlier investigations, monitoring tools, and reporting outputs.
- Monitor portfolio risk and limits, identifying emerging risks and escalating issues where appropriate.
- Ensure full documentation and reproducibility of models and analytics.
- Work closely with Tech and Data Engineering teams to improve the analytics platform, focusing on data quality, reconciliation, and scalability.
- Standardise and automate analytics and reporting outputs to improve efficiency and consistency.
- Structure and harmonise heterogeneous datasets into consistent portfolio-level views.
- Be a cross-functional partner to teams to support portfolio construction, origination assessment, investor relations, and strategic initiatives.
- Action reports for regulatory, audit, and governance processes.
What they're looking for
- At least 2 years of experience in data analytics within private credit, asset-backed finance, securitisation, or related fields.
- Understanding of ABF / securitisation fundamentals, including collateral analysis, portfolio monitoring, and structure mechanics (e.g. waterfalls, triggers).
- Advanced Python, (especially Pandas) skills for data analysis, modelling, and production-ready code.
- Knowledge of git, CI/CD and collaborative development environments
- Intermediate SQL skills
- Ability to analyse concentration, exposure dynamics, and performance trends across portfolios.
- Ability to translate business and risk requirements into analytical outputs.
- Strong communication skills, with the ability to present insights to technical and non-technical audiences in a structured and actionable way.
Nice to have
- Basics of system design
- Strong grasp of portfolio risk concepts and early warning indicators.
- Experience in structuring or exposure to securitisation transactions.
- Familiarity with scenario analysis and stress testing frameworks.
- Understanding of data engineering concepts (data pipelines, ingestion, transformation).
- Exposure to fintech lending platforms or non-bank lending environments.
- Experience working with multiple datasets from different sources, including data integration, standardisation and reconciliation.
- Understanding of cloud-based environments (AWS preferred)
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
About Fasanara
Founded in 2011, Fasanara is a global asset manager and technology platform.
We manage c. USD 6 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.110 employees globally, we are a pioneer investor in Fintech Lending, enabling Real Economy Impact.
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 and excellence. There is no bureaucracy, typical of large organisations. The environment is collaborative, entrepreneurial, and trust based. We set ambitious goals, work extremely hard, stress the importance of teamwork, and adhere to the highest level of excellence in everything we do. We are only as good as our team. Thus, we are building the firm around exceptional talent.
Responsibilities
Portfolio Risk Analytics & Insights
- Contribute to the development of portfolio risk analytics across ABF strategies, including: concentration, exposure, performance tracking, stress testing, outlier investigations, monitoring tools, and reporting outputs.
- Monitor portfolio risk and limits, identifying emerging risks and escalating issues where appropriate.
- Ensure full documentation and reproducibility of 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.
- Standardise and automate analytics and reporting outputs to improve efficiency and consistency.
- Structure and harmonise heterogeneous datasets into consistent portfolio-level views.
Stakeholder Collaboration
- Be a cross-functional partner to teams to support portfolio construction, origination assessment, investor relations, and strategic initiatives.
- Action reports for regulatory, audit, and governance processes.
Requirements
Essential
- At least 2 years of experience in data analytics within private credit, asset-backed finance, securitisation, or related fields.
- Understanding of ABF / securitisation fundamentals, including collateral analysis, portfolio monitoring, and structure mechanics (e.g. waterfalls, triggers).
- Advanced Python, (especially Pandas) skills for data analysis, modelling, and production-ready code.
- Knowledge of git, CI/CD and collaborative development environments
- Intermediate SQL skills
- Ability to analyse concentration, exposure dynamics, and performance trends across portfolios.
- Ability to translate business and risk requirements into analytical outputs.
- Strong communication skills, with the ability to present insights to technical and non-technical audiences in a structured and actionable way.
Desirable
- Basics of system design
- Strong grasp of portfolio risk concepts and early warning indicators.
- Experience in structuring or exposure to securitisation transactions.
- Familiarity with scenario analysis and stress testing frameworks.
- Understanding of data engineering concepts (data pipelines, ingestion, transformation).
- Exposure to fintech lending platforms or non-bank lending environments.
- Experience working with multiple datasets from different sources, including data integration, standardisation and reconciliation.
- Understanding of cloud-based environments (AWS preferred)
- Interest in leveraging AI tools and technologies to enhance analytics capabilities.
Benefits
Our benefits include:
- Performance-related Bonus Scheme
- Private Medical & Dental Insurance through Bupa (including children's cover)
- Life Insurance, Income Protection & Critical Illness Cover
- Employee Assistance Programme (EAP)
- Salary sacrifice pension with Hargreaves Lansdown (currently matched up to 10%)
- Enhanced Maternity & Paternity Leave
- Emergency Parental Leave, Emergency Childcare & Nursery Benefit
- Cycle to Work Scheme
- Charity Giving through CAF
- Annual Wellbeing Budget
- Team socials, annual Summer & Christmas parties, and other events throughout the year
Company
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