About the job
Optasia is a B2B2X financial technology platform focused on scoring, financial decision-making, disbursement, and collection. The company’s mission centers on advancing financial inclusion worldwide and reshaping the financial sector through a distinct approach.
Role Overview
The Quantitative Risk Data Scientist will join the Credit Portfolio Optimization team in Athens. This position sits at the intersection of risk management, research, and technology, contributing to algorithmic trading and portfolio optimization projects. The role involves working alongside traders, big data experts, and machine learning engineers to support real-time decision-making and maintain strong system performance.
What You Will Do
- Design and implement algorithmic solutions to maximize revenue through detailed credit risk analysis.
- Extract actionable insights on credit risk using advanced big data analytics.
- Develop tools and procedures for portfolio risk assessment to monitor and manage credit risk effectively.
- Conduct thorough risk analyses of microloans and other financial products, refining risk models to improve decision quality.
- Identify and evaluate credit risk factors by applying advanced computational techniques to large datasets.
- Build predictive models using statistical and machine learning methods to strengthen risk management strategies.
- Collaborate closely with data scientists and machine learning engineers.
- Continuously update risk assessment methods in response to changing market conditions.
What We Look For
- Bachelor’s or Master’s degree in Data Science, Statistics, Finance, Mathematics, or a related discipline.
- 2-5 years of experience in quantitative risk analysis, preferably within financial services.
- Demonstrated skill in developing and applying algorithmic models for revenue and risk optimization.
- Strong background in statistical modeling and experience with machine learning models.
- Proficiency in Python or R, and hands-on experience with big data risk analytics.
- Track record of designing and deploying portfolio risk assessment tools.
- Excellent problem-solving skills and attention to detail when working with complex datasets.
Key Attributes
- Sound judgment and strong analytical thinking.
- Self-driven, resourceful, and comfortable working independently.

