Data Analyst, Portfolio management 

PRELIMINARY

Role Mission

• Source, model and quality-assure portfolio, collateral, arrears and behavioral data for the portfolio management squad, sizing the customer segments and running the core analyses:

  • Bounce-back and cure
  • Rollforward
  • Repeat delinquency
  • Early-repayment
  • Post-closure relationship erosion
  • Collateral surrender

• Build dashboards, early warning reporting and insights that allow the Squad Lead, Product Owner and Business Analyst to test the POC hypotheses, track KPIs and make data-driven prioritization decisions.
   

Responsibilities

• Develop and maintain reliable portfolio data, including mortgage portfolio, collateral, overdue payments, and customer behavior data, ensuring accuracy, completeness, and consistency.

• Analyze customer and portfolio data to understand the drivers of default, distress, and customer attrition, and when these outcomes start to emerge.

• Identify risk signals, customer segments, and critical moments where proactive intervention could prevent adverse outcomes or improve retention.

• Build and maintain dashboards and reports that provide visibility into:

  • Portfolio performance
  • Customer risk migration
  • Remediation progress
  • Key operational KPIs

• Evaluate the effectiveness of portfolio strategies by analyzing customer outcomes, such as:

  • Cure rates
  • Redefault rates
  • Non-performing loan (NPL) migration
  • Recommend improvements based on insights

• Collaborate with the Data Scientist and Credit Risk team by supplying model-ready data, supporting model validation, and reporting deployed model performance. 

• Support the Business Analyst by validating data availability, assessing the feasibility of business requirements, and providing analytical evidence for business cases, testing, and solution implementation
   

Experience

• 5+ years in data analytics, risk analytics or portfolio MIS in banking, financial services or another data-intensive industry.

• Hands-on SQL, Python or R and data modeling across large datasets. 

• Hands-on Power BI, Tableau or similar tools, including dashboard build and automation. 

• Experience with lending or portfolio risk data such as:

  • LTV
  • DTI
  • Roll rates
  • Provisioning preferred. 

• Nice to have, trainable on-the-job: Mortgage domain knowledge. 

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Accountability (Example of KPIs)

Portfolio Quality

• NPL ratio
• Roll rate
• Risk-tier migration
• Cure rate
• Redefault rate
• Recovery and remediation success rate

Customer Experience & Retention

• NPS
• Early exit rate
• Post-closure relationship retention
• Cross-holding retention 

Data Quality & Reliability

• Data completeness rate
• Data accuracy rate
• Report timeliness
• Data issue resolution time 

Knowledge & Skills

• Proficient in product knowledge: Knowledge of lending products, portfolio metrics, delinquency management and credit risk concepts; mortgage specifics are trainable. 

• Competent in data-driven decision-making: Strong SQL and data querying skills, with experience managing and integrating large datasets; experience building interactive dashboards and automated reports using tools such as Power BI or Tableau.

• Advanced beginner in agile execution: Understanding of agile concept, work to sprint cadence with the squad, sizing analytics tasks and delivering data and reporting increments each sprint. 

• Advanced beginner in problem-solving: Ability to assess portfolio performance, customer behavior and risk trends and generate actionable business insights.

• Advanced beginner in risk & control management: Understanding of data quality, validation, documentation and governance practices to ensure reliable and auditable reporting. 

• Advanced beginner in strategic mindset: Ability to prioritize analyses to fit the business priority and requirements. 

• Proficient in customer focus: Understand customer and operational pain points through data to identify friction points and improvement opportunities.

• Advanced beginner in stakeholder balance: Build trusted relationship with Risk, Finance, Technology and Data teams on data definitions, priorities and reporting standards.

• Advanced beginner in result-driven: Translate analysis into practical recommendations that enable timely business action. 

• Advanced beginner in collaboration: Collaborate with Business Analysts, Credit Risk, Finance and Data teams to deliver shared outcomes

 

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If you receive any suspicious communication claiming to be from Manpower, please report it immediately to: [email protected]

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