At a Glance
- Tasks: Analyse data and develop tools for structured finance transactions.
- Company: Join Five Sigma, a leader in private-credit and asset-backed finance.
- Benefits: Competitive salary, flexible work environment, and growth opportunities.
- Other info: Dynamic start-up culture with opportunities for personal and professional development.
- Why this job: Make an impact in finance with innovative technology and analytics.
- Qualifications: STEM or Finance graduate with Python skills and analytical mindset.
The predicted salary is between 40000 - 50000 £ per year.
Five Sigma helps investors, originators and asset owners execute, monitor and scale private-credit and asset-backed-finance transactions through specialist advisory services and ABIDE, its proprietary asset-management platform.
Our proprietary technology platform, ABIDE, combines data, analytics and workflow automation to deliver market-leading insights across complex loan portfolios and structured finance transactions.
As we continue to grow, we are looking for an analytical and commercially minded
Data & Loan Portfolio Analytics Analyst to join our expanding Structured Finance team.
Key Accountabilities
Assist in the execution of Five Sigma structured finance activities across the product offering:
- Develop and maintain portfolio data pipelines and data validation checks.
- Develop and maintain analytical tools for the benefit of the Transaction Lifecycle Advisory team.
- Review transaction documentation to verify the portfolio adherence to qualitative and quantitative transaction limits.
- Liaise with originators, issuers, servicers, and other third-party providers to resolve data related queries.
- Develop and maintain analytics in relation to Transaction Monitoring (through the ABIDE platform).
- Occasionally, assist with the execution of buy or sell side advisory mandates (analytics, modelling, financial due diligence).
Collaborate with wider analytics team with the development and maintenance of new risk management / analytical tools, including:
- Risk and pricing tools for granular asset portfolios (mortgages, SME and consumers loans, cards, etc.)
- Analytics and forecasting tools for granular asset portfolios and other macro variables.
Conduct research independently and present results to internal and external stakeholders.
Conduct ad-hoc data analysis tasks.
Liaise with software engineering team to implement research outputs and strategies into our Saas product offering.
Candidate Experience
- STEMs or Finance graduate.
- 1 to 3 years of work or internship experience in a similar role.
- Good working knowledge of Python/software development.
- Data skills, including aggregation, transformation, and analysis of small to medium-size datasets preferred.
- Self-motivated / Self-starter / entrepreneurial mind, ability to work well in a start-up like environment.
Desirable
- Working knowledge of programming and software development practices.
- Working knowledge of Fixed Income / Credit / ABS markets in a quantitative finance setting.
- #J-18808-Ljbffr
Analyst, Data & Loan Portfolio Analytics employer: Five Sigma Finance
At Five Sigma, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. As a Senior Engineer, you'll not only contribute to our cutting-edge asset management platform but also have the opportunity for personal and professional growth by working closely with our leadership team. Our supportive environment encourages continuous learning and inclusivity, making it a rewarding place to advance your career in the dynamic financial technology sector.
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We think this is how you could land Analyst, Data & Loan Portfolio Analytics
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We think you need these skills to ace Analyst, Data & Loan Portfolio Analytics
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
