Quantitative Risk Data Scientist, Fintech

Optasia · Greece · posted today ago

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About this role

Optasia is a real-time financial intelligence company. We build precision intelligence that unlocks financial opportunity at scale, running millions of automated credit decisions every day across high-growth markets where formal credit data is limited. Our AI-powered platform turns permitted behavioural data and real-time signals into precise, responsible credit decisions. Embedded within the systems of telcos, banks, digital wallets and other partners, it helps them extend relevant financial services to more people while maintaining strong portfolio control and opens profitable new segments that were previously out of reach. This is how Optasia turns potential into financial progress, responsibly and at scale.

We are seeking for enthusiastic professionals, with energy, who are results driven and have can-do attitude, who want to be part of a team of likeminded individuals who are delivering solutions in an innovative and exciting environment. Quantitative Risk Data Scientist significant contributors of Optasia’s advanced algorithmic trading and portfolio optimization unit. As member of the Credit Portfolio Optimization team in Optasia, you will have an opportunity to combine the disciplines of risk management, research, and technology to operate trading strategies across multiple projects. You are expected to leverage your experience and knowledge in making critical decisions in real-time processes, while working closely with experienced traders, big-data, and machine-learning engineers to ensure optimal system performance.

What you will do

Design and implement algorithmic solutions for revenues optimization though credit risk analysis.
Deliver credit-risk insights through big data risk analytics.
Development and implementation of portfolio risk assessment tools and procedures to monitor and manage credit risk.
Conduct detailed risk analysis on microloans and other financial products, optimizing risk models to improve decision-making.
Identify and evaluate credit risk factors using advanced computational methods on large datasets.
Develop predictive models, including both statistical and machine learning approaches, to support risk management strategies.
Collaborate closely with data scientists and data & machine learning engineers.
Continuously refine and improve risk assessment methodologies to align with evolving market conditions.

What you will bring

Bachelor’s or master’s degree in data science, Statistics, Finance, Mathematics, or a related field.
Proven experience (2-5 years) in quantitative risk analysis, preferably within the financial services sector.
Expertise in developing and implementing algorithmic models for revenue and risk optimization.
Strong proficiency in statistical modeling, with experience in machine learning models.
Proficiency in programming languages such as Python or R, with extensive experience in big data risk analytics.
Demonstrated ability to develop and implement portfolio risk assessment tools.
Strong problem-solving skills and attention to detail in analyzing complex data sets.

Your key attributes

Excellent judgment and problem-solving skills
Self-motivated, self-directed, resourceful and in possession of a high level of personal initiative
Communication skills
Creative skills
Experience in working with secure code development guidelines and coding practices (i.e. OWASP, NIST)

Why Optasia?

Optasia is a high-growth, AI-powered financial infrastructure platform serving over 432 million users in emerging markets across Africa and Asia. By harnessing the power of AI and real-time data we unlock financial opportunities for customers. We have been recognized as a top fintech by Forbes Middle East, culminating in a successful JSE listing in 2025.

At Optasia, our global team builds the tech, products and services which will change the game for the world's unbanked population. We empower our people by offering unique employee experiences and support individual careers by prioritizing well-being and embracing flexibility.