Assistant Manager - Data Science

Designation: Assistant Manager ? Data Science

Level : L3

Experience:  6 to 10
years

Location :  Colombo

Job
Description:

We are
looking for experienced Data Scientist to join our team.

Responsibilities:

• Decision
  Science ? Credit Risk Modelling and Portfolio Optimization

• Build
  statistically robust application and behavior scorecards; develop PD, LGD, and
  EAD models

• Design loss ? forecasting frameworks (vintage, roll ? rate/Markov,
  survival/hazard, GLM/GBM) at segment and portfolio levels.

• Engineer
  features from bureau, internal behavioral, transaction, device, and alternative
  data with rigorous data QC.

• Calibrate and
  backtest models; perform stability monitoring (PSI/CSI), discrimination
  (KS/AUC), and calibration tests.

• Implement
  explainability (reason codes/SHAP), bias/fair ? lending
  checks, and challenger/benchmark models.

• Align models
  to accounting and capital frameworks (IFRS 9 or CECL; Basel PD/LGD/EAD concepts).

• Development
  of credit card portfolio optimization data models to drive spends and
  engagement

• Portfolio
  Spends Growth ? Targeting basis segments/persona, merchants, campaign recommendation
  to drive cross border

Skills:

• Must have
  working knowledge of credit cards

• Must have
  hands on knowledge of building application score, behavior score, PD, LGD
  models

• Solid
  understanding of IFRS 9 or CECL (lifetime ECL, staging/pooling), and model risk
  governance

• Solid
  understanding of credit card lifecycle and credit card portfolio interventions

• Python, ML,
  SQL, SAS and Tableau

• Communication
  skills to explain complex methods to non ? technical
  audiences; clear documentation.

• Exposure to
  bureau data (e.g., attributes, reason codes), alternative data, and identity/behavioral
  signals.

• Exposure to
  transaction level credit card spends data

• Credit risk
  modelling (IFRS 9, Python, ML, SQL, SAS and Tableau,PD, LGD models)

• Communication
  skills to explain complex methods to non ? technical
  audiences; clear documentation.

 

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