[Hiring] Data Scientist @MIT Recruitment

Role Description

As a Senior Data Scientist within the Credit Risk team, you will design, develop, and implement models that drive lending decisions and portfolio risk management. You will work across the full modelling lifecycle — from problem definition and data exploration to model development, validation, and deployment within a modern cloud environment. The role combines deep credit risk expertise with hands-on data science and engineering, with a focus on building robust, production-ready models. You will play a key role in shaping how we assess risk, forecast portfolio performance, and optimise credit strategies across our global lending platform.

Responsibilities

• Explore and understand internal and external data to gain valuable insights and to enrich our analysis and machine learning pipelines
• Develop and maintain credit risk models, including PD, LGD, and EAD
• Build and enhance cash flow and portfolio forecasting models
• Design and implement predictive models using Python and machine learning techniques
• Work with large, complex datasets to perform feature engineering and model optimisation
• Collaborate with data and engineering teams to deploy models in AWS (e.g., SageMaker)
• Support model monitoring, validation, and governance processes
• Analyse portfolio trends and provide insights to improve credit strategy and underwriting decisions
• Contribute to IFRS 9-style forecasting and risk reporting frameworks
• Communicate technical concepts and model outputs clearly to stakeholders

Qualifications

• Bachelor’s, Honour’s or Master’s degree in a quantitative field (e.g., Mathematics, Statistics, Actuarial Science, Engineering, Economics, or similar)
• 5+ years of experience in credit risk, data science, or quantitative modelling roles
• Proven experience developing PD, LGD, or EAD models
• Strong programming experience in Python (e.g., pandas, numpy, scikit-learn)
• Solid experience with SAS/SQL and working with large datasets
• Experience working with AWS-based tools, particularly SageMaker (or similar cloud platforms)
• Familiarity with version control tools (e.g., GitHub)
• Experience with portfolio forecasting, cash flow modelling, or IFRS 9 frameworks is advantageous
• Experience implementing models in a production environment (particularly SageMaker)
• Background in fintech, lending, or financial services preferred

Requirements

• Passionate about Data Science
• Analytical thinking capability; be logical, systematic, strategic and pragmatic
• Strong foundation in credit risk modelling (PD, LGD, EAD)
• Ability to translate business problems into practical, data-driven solutions
• Strong attention to detail, both quantitative and qualitative, can organize large amounts of data from disparate sources
• Mindfulness; be considerate of the implications of your work, really care about what you are doing and the impact of your contribution
• Advanced Python-based modelling and data analysis
• Building models that are not just theoretically sound, but deployable and scalable in production
• Working across both statistical modelling and machine learning approaches
• Developing forecasting and cash flow models for lending portfolios
• Navigating and working within cloud-based environments (AWS)
• Writing efficient, reliable SAS/SQL queries for large datasets
• Communicating insights clearly to both technical and non-technical stakeholders
• Operating independently and taking ownership of end-to-end modelling