Data Scientist - Mortgage Valuation & Risk (Night Shift)

JP Morgan Chase

Manila

On-site

PHP 600,000 - 1,200,000

Full time

14 days+

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Job summary

JPMorganChase in Taguig City, National Capital Region, Philippines, seeks a Data Scientist in the Mortgage Servicing Rights team to support portfolio valuation and risk management. You will partner with quantitative modelers, business stakeholders, and tech teams to design and deliver tools and analyses, ensuring timely, well-tested, and documented outputs.

You will apply strong data management discipline, governance, and security standards to ensure trusted results, translating complex data

Qualifications

Bachelor’s degree in financial engineering, statistics, mathematics, computer science, or a related field.
Two years of experience in data-focused development or analytics roles.
Advanced Python proficiency for data analysis and production-quality code.
Proficiency with SQL for data extraction, transformation, and analysis.
Experience with machine learning and statistical modeling methods, including classification, regression, and time-series forecasting.
Knowledge of fixed income analytics and financial modeling concepts relevant to valuation and risk work.
Experience applying generative AI methods to business problems, including selecting an appropriate approach.
Ability to translate complex concepts into clear, decision-ready communication for varied audiences.
Strong software and systems analysis skills, including requirements translation and solution design.
Ability to manage multiple deliverables with strong attention to detail and execution discipline.

Responsibilities

Assemble, organize, and analyze data using proprietary, industry-standard, and open-source sources and tools.
Develop, test, and maintain Python utilities that support valuation and risk analytics workflows.
Support valuation model and risk processes by managing data access, data inputs, and local implementation needs.
Gather requirements and implement analytical tooling into a proprietary analytics platform used by the team.
Translate business needs into datasets and data models that support valuation, back-testing, and analysis.
Apply modern product delivery practices to improve solution usability, reliability, and reuse.
Manage projects end-to-end, including scope, milestones, dependencies, and execution quality.
Deliver data-driven insights and analytical support to quantitative modelers and business stakeholders.
Ensure adherence to data governance, security standards, and documentation expectations.
Document processes, definitions, and assumptions to enable repeatable analysis and audit-ready outputs.
Identify and implement opportunities to improve data tools, automation, and analytics capabilities.