Location: Metro Manila, National Capital Region, Philippines

Bring your expertise to a role where advanced analytics directly supports valuation and risk management decisions. Youll own projects end-to-end, from requirements through implementation and measurable outcomes. Youll help strengthen data governance and reliability while improving how analysis is delivered and consumed. If you enjoy translating complex data into clear, decision-ready outputs, this role is for you.

Job summary :
As a Data Scientist in the Mortgage Servicing Rights team, you use data science and analytics to support portfolio valuation and risk management. You partner with quantitative modelers, business stakeholders, and technology teams to design and deliver tools and analyses that improve decision-making. You manage delivery end-to-end, ensuring outputs are timely, well-tested, and documented. You apply strong data management discipline and follow applicable governance and security standards to ensure trusted results.

Job 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

 

Required qualifications, capabilities, and skills:

• Bachelors 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 artificial intelligence 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
• Ability to work independently and collaboratively in a fast-paced environment

 

Preferred qualifications, capabilities, and skills:

• Masters degree in financial engineering, statistics, mathematics, computer science, or a related field
• Experience with performance analysis and code optimization
• Familiarity with mortgage loan data and common mortgage analytics concepts (for example, prepayment and cash flow modeling)
• Experience integrating analytics tools into proprietary platforms used for valuation or risk workflows
• Experience in financial services, particularly mortgage analytics or risk management
• Demonstrated ability to deliver results under tight timelines while maintaining quality
• Strong presentation skills for communicating insights and recommendations to stakeholders

 

Use data science to support mortgage servicing rights valuation and risk decisions through models, tools, and analytics.