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Data Scientist – Fair Value squad at ING, Makati, focusing on Python model development in regulated lending.
Drive fair‑valuation models, analyze spreads, and collaborate with cross‑functional teams in a digital banking environment.
Think Forward
At ING, our purpose is to empower people to stay a step ahead in life and in business. We are a digital bank at scale, continuously evolving how we serve our customers and colleagues through technology, data, and innovation.
Within Wholesale Banking Lending, the Fair Value squad develops and maintains valuation capabilities used for pricing transparency, portfolio insight, and regulatory alignment across lending products. The squad combines quantitative finance, statistical modeling, and production‑grade data science to deliver robust valuation solutions in a regulated environment.
Your role and work environment
You will join the Fair Value squad within the Wholesale Banking Lending tribe. The squad focuses on fair valuation of lending portfolios, including modelling of cash flow behaviour, discounting, spreads, and risk‑related valuation components.
In this role, you will contribute to the design, implementation, monitoring, and improvement of production‑grade fair value models and supporting data pipelines. You will work on model components such as spread estimation, regression‑based proxy models, portfolio analytics, and valuation parameter application across the lending landscape.
You will operate in a multidisciplinary environment together with Data Scientists, Quants, Java engineers, DevOps engineers, and business stakeholders. The work requires strong technical skills, sound quantitative judgement, and the ability to deliver reliable and explainable outputs in a controlled banking environment.
The team
You will work in a multidisciplinary environment with:
- Data Scientists and Quant specialists
- Java and platform engineers
- DevOps and production support teams
- Risk, Finance, and Lending domain experts
- Model validation and control stakeholders
The Fair Value squad works at the intersection of quantitative methodology, production engineering, and regulatory control, with strong focus on collaboration, robustness, and traceability.
Key responsibilities
- Develop and maintain Python‑based model code, data transformations, and monitoring pipelines
- Translate quantitative valuation concepts into robust and maintainable data science implementations
- Work on regression‑based and statistical models used in fair value components such as spread estimation and proxy modelling
- Support modelling of discounting, valuation assumptions, and risk‑related inputs used in fair value calculations
- Ensure outputs are explainable, traceable, reproducible, and suitable for use in a regulated environment
- Contribute to model monitoring, performance analysis, and periodic model reviews
- Investigate data quality, feature behaviour, and model performance issues in production
- Collaborate with Java engineers on how spread parameters and valuation logic are applied in downstream systems
- Contribute to technical design decisions, implementation improvements, and documentation
- Partner with Risk, Finance, Lending, and validation stakeholders to support transparency and governance
What you will deliver
- Reliable fair value model implementations and supporting analytics for lending portfolios
- Python‑based production solutions for model execution, monitoring, and reporting
- Improved transparency and robustness in valuation outputs and model behaviour
- Well‑documented and reproducible modelling components aligned with governance expectations
- Insights into spread behaviour, model performance, and valuation drivers
- Technical contributions that improve maintainability, control, and scalability of fair value solutions
Requirements
- MSc or PhD in Data Science, Statistics, Econometrics, Quantitative Finance, Mathematics, Computer Science, or a related field
- Strong Python engineering experience with the ability to build and maintain production‑grade model implementations
- Strong knowledge of statistics, regression, and time‑series or panel‑data methods
- Experience developing, validating, or monitoring predictive or explanatory models in production environments
- Ability to translate quantitative or financial concepts into robust data and model implementations
- Solid analytical skills and comfort working with valuation, pricing, discounting, or risk‑related concepts
- Experience with data pipelines, testing, version control, and production support practices
- Ability to independently validate results, investigate model behaviour, and explain outcomes clearly
- Experience working in a regulated environment with strong expectations on controls, traceability, and reproducibility
- Strong communication skills and ability to collaborate across technical and non‑technical stakeholders
Preferred Skills (Nice‑to‑Have)
- Experience in banking, lending, risk, or valuation use cases
- Familiarity with discounted cash flow methods, spread‑based discounting, or proxy modelling approaches
- Experience with SOx‑critical data pipelines and model controls
- Working knowledge of Java, especially for understanding application logic linked to spread parameters and discounting
- Experience with model monitoring frameworks and performance reporting
- Familiarity with DevOps practices, CI/CD, and deployment workflows
- Experience collaborating with model validation or audit stakeholders
- Ability to mentor junior colleagues and contribute to squad‑level technical direction