As a Senior Data Scientist, you will play a critical role in driving data-driven decision-making across our lending business. You will leverage advanced analytics, machine learning, and statistical modeling to improve credit risk strategies, optimize lending decisions, and enhance portfolio performance. Working closely with Risk, Portfolio Management, Product, Engineering, and Finance teams, you will develop and deploy data science solutions that directly impact business growth while maintaining sound risk management practices.
Nature of Work
- Partner closely with stakeholders across Credit Risk, Lending, Portfolio Management, Product, Engineering, and Finance to identify opportunities where data science can drive measurable business outcomes.
- Develop and implement machine learning, statistical, and predictive models to support credit risk assessment, underwriting, portfolio monitoring, customer segmentation, collections optimization, and fraud prevention initiatives.
- Analyze large and complex datasets to uncover trends, identify risk drivers, and generate actionable insights that improve lending performance and customer outcomes.
- Design, evaluate, and monitor model performance using appropriate risk and business metrics, ensuring compliance with regulatory and governance requirements.
- Collaborate with Data Scientists, Data Engineers, and MLOps Engineers to productionize models and continuously improve model monitoring, deployment, and operational processes.
- Conduct experimentation and advanced analytics to optimize credit policies, risk strategies, pricing decisions, and lending product performance.
- Stay current with emerging developments in machine learning, risk analytics, explainable AI, and financial services data science, bringing innovative approaches into the organization.
Required Qualifications
- Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Economics, Engineering, or another quantitative discipline. Master's or PhD is a plus.
- At least 3 years of hands-on experience in Data Science, Machine Learning, Advanced Analytics, or Quantitative Modeling.
- Strong preference for candidates with experience in:
- Consumer lending, digital lending, banking, fintech, credit cards, or other financial services environments.
- Credit risk modeling, underwriting analytics, portfolio risk management, collections analytics, or fraud risk analytics.
- Building predictive models such as probability of default (PD), propensity models, risk scorecards, customer lifetime value models, or other risk-related analytical solutions.
- Strong proficiency in Python, SQL, and machine learning frameworks, with experience handling large-scale structured and unstructured datasets.
- Experience with statistical techniques including regression, classification, clustering, causal inference, and explainable AI.
- Working knowledge of model governance, validation, monitoring, and regulatory considerations within financial services is highly desirable.
- Strong business acumen with the ability to translate complex analytical findings into actionable recommendations for both technical and non-technical stakeholders.
- Excellent communication, stakeholder management, and problem-solving skills.
