Job Description

:

We are looking for an experienced

Modelling Analytics Specialist

with a strong background in

banking and financial services analytics

. The role involves developing predictive and statistical models to optimize marketing, risk, and customer lifecycle strategies. The ideal candidate will combine technical expertise in data science with deep business understanding to generate insights that improve acquisition, cross-sell, retention, and portfolio health across retail and commercial banking segments.

Key Responsibilities:

Develop and deploy predictive models

for use cases such as credit risk, customer segmentation, attrition prediction, and marketing campaign optimization.

Apply advanced machine learning and statistical techniques

to derive actionable insights from large, structured, and unstructured banking datasets.

Collaborate closely with business stakeholders

(e.g., marketing, credit risk, collections, customer experience teams) to define analytical problems and deliver measurable business impact.

Perform exploratory data analysis and feature engineering

to identify key drivers of business outcomes.

Leverage tools such as Python, R, and SQL

for model development, validation, and automation.

Create data visualizations and dashboards

(Tableau, Power BI, etc.) to communicate insights effectively to senior business and technical leaders.

Monitor and maintain model performance

post-deployment, ensuring models stay compliant and relevant to changing market dynamics.

Ensure regulatory compliance and data governance

across all modelling activities in alignment with banking policies.

Stay current with emerging modeling methodologies

and innovations in AI/ML relevant to financial analytics.

Qualifications & Requirements:

6+ years of experience

in statistical modelling, predictive analytics, or machine learning within the

banking or financial services domain

. Strong understanding of

banking data structures

-- including customer, transaction, credit, and product data.

Proficiency in Python, R, SQL

and familiarity with modelling libraries (e.g., Scikit-learn, XGBoost, TensorFlow). Strong grasp of

statistical concepts

(e.g., regression, classification, time-series forecasting, hypothesis testing). Proven ability to

translate analytical findings into business recommendations

with clear ROI impact. Experience with

data visualization tools

such as Tableau or Power BI (good to have) Bachelor's or Master's degree in

Statistics, Mathematics, Data Science, Computer Science, Economics, or related field.

Preferred Skills (Good to Have):

Experience in

credit risk modelling

,

marketing mix modelling

, or

customer lifetime value analysis

. Exposure to

cloud-based analytics platforms

(AWS, Azure or GCP). * Familiarity with

model risk governance frameworks

and regulatory expectations (e.g., SR 11-7, CCAR, Basel).