Data Scientist [Integrated Risk Management]

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We are looking for a Data Scientist for our Integrated Risk Management team to help develop risk models and analytical frameworks that support key business decisions across credit, financial, and operational risk domains. In this role, you will work on complex quantitative challenges, build predictive and diagnostic models, and partner with stakeholders across the organization to improve risk visibility, portfolio performance, and decision-making. Challenges that await you: Develop and implement advanced statistical models for our credit portfolio at both per-account and aggregate levels to predict behavior and optimize performance Create sophisticated Net Present Value (NPV) models and their core components on a per-account basis for our various credit products Model critical financial metrics, including FX position, liquidity, reserves, and other balance sheet items to support robust financial management Analyze and model not only expected outcomes but also their deviations, distributions, and uncertainty, recognizing the inherently probabilistic nature of financial and risk data Design diagnostic and forecasting models that help identify, monitor, and mitigate risks across the organization Partner with cross-functional stakeholders to investigate model performance, understand deviations from expectations, and improve decision-making processes Contribute to the development of quantitative methodologies and risk frameworks across credit, financial, and operational risk domains What makes you a great fit: M.S. or Ph.D. in a quantitative field such as Statistics, Computer Science, Mathematics, Physics, Economics, or a related discipline 4+ years of hands-on experience in Data Science, Quantitative Analytics, Risk Modeling, or a similar role Strong understanding of statistical modeling, probability theory, and uncertainty quantification Experience developing predictive models and working with financial, risk, or other highly stochastic datasets Strong proficiency in Python or R and experience with statistical and analytical libraries (e.g., Pandas, NumPy, SciPy, Statsmodels, Scikit-learn) Experience with classical statistical methods, forecasting techniques, and modern machine learning approaches when appropriate Experience modeling distributions, confidence intervals, and risk metrics rather than focusing solely on point estimates Strong problem-solving skills and ability to independently drive analytical initiatives from problem definition to implementation Excellent communication skills with the ability to explain complex concepts to both technical and non-technical stakeholders Our ways of working: Innovative Spirit: a commitment to creativity and groundbreaking solutions Honest Feedback: valuing open, transparent communication Supportive Team: a strong, collaborative community Celebrating Achievements: recognizing our wins together High-Tech Environment: a team of smart and ambitious people who challenge the status quo of traditional finance

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