Credit Portfolio Risk Optimizer
hace 6 días
About the Role
We are looking for a highly skilled data scientist to join our Actuarial Portfolio Management, Credit and Surety team.
You will contribute to the analysis of the credit and surety portfolio, from both risk and profitability perspectives, through the advanced analysis of internal and external data.
This is a unique opportunity to apply your deep knowledge of data science and analytics to real portfolios, connect economic trends to market performance, and investigate patterns to ensure long-term profitability.
In this role, you will work closely with colleagues from various departments, including underwriting, finance, operations, claims, risk management, and actuarial functions.
Key Responsibilities:
- Contribute to steering and monitoring the portfolio in line with strategic objectives, maintaining oversight of the Global Surety portfolio performance across regions.
- Support activities related to the elaboration and implementation of the strategy for Actuarial Portfolio Management Credit & Surety.
- Collaborate with cross-functional teams to develop innovative solutions that support data transformation, risk management, and underwriting efficiency.
About You
You have strong interpersonal skills and ability to maintain effective working relations in a multi-disciplinary and multi-cultural environment.
You are a self-starter and organized, able to handle multiple priorities and meet deadlines.
You are able to apply quantitative skills, business knowledge, logical thinking and communicate the key message by visualization & presentation.
Requirements:
- 6+ years of hands-on experience with PySpark and Python, particularly in building and optimizing large-scale data transformation pipelines.
- 3+ years of SQL experience, comfortable writing and optimizing complex queries.
- Solid experience developing NLP solutions, with exposure to GenAI and unsupervised learning methodologies.
- Extensive experience in data visualization and developing interactive dashboards for business stakeholders, with a focus on strategic insights.
- Good understanding of statistical models and core statistical methods, like distributions, time series, etc.
- Solid academic education (university degree or higher), ideally in financial economics, statistics, econometrics, financial engineering, or actuarial sciences.
- Advanced English communication skills, both written and verbal.
- Proven ability to work independently, solve complex problems, and drive business impact through data-driven strategies.
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