Lead Business Analytics

Role Details:

Responsible for driving Business Intelligence, Advanced Analytics, Machine Learning and GenAI initiatives across Retail Assets businesses . The role will leverage data, analytics and AI-driven insights to support portfolio growth, customer acquisition, portfolio monitoring and strategic decision-making, while partnering with Business, Risk, Product, IT and Data Engineering teams to deliver measurable business outcomes.

Overall, Job Description

• Lead Business Intelligence, Advanced Analytics and data-driven decision-making initiatives across Retail Assets businesses.
• Partner with business stakeholders to generate actionable insights and support strategic initiatives.
• Develop predictive analytics and machine learning use cases for lead prioritisation, customer segmentation, portfolio monitoring and business growth initiatives.
• Build analytics and machine learning frameworks using SQL, Python, PySpark, Databricks and SAS to support customer acquisition, portfolio growth and risk management.
• Develop dashboards, MIS, scorecards and self-service analytics solutions using Power BI.
• Collaborate with Data Engineering teams on data marts, dimensional models and scalable data pipelines.
• Drive adoption of Advanced Analytics, Machine Learning, GenAI and Agentic AI solutions for business intelligence, portfolio monitoring and process automation.
• Lead and mentor analytics teams and manage stakeholder engagements across Business, IT, Data Engineering and SDG.

Education and Work Experience Requirements:

Essential requirements:

Bachelor's or Masters degree in Engineering, Computer Science, Information Technology, Statistics, Mathematics, Economics, Data Science or a related quantitative discipline.

Preferred:

MBA/PGDM or a Master's degree from a reputed institute, with specialisation or certification in Analytics, Data Science, Artificial Intelligence, Machine Learning or Business Intelligence. Relevant Databricks certifications would be an added advantage.

Essential requirements:

8-12 years of experience in Banking Analytics and Business Intelligence, with strong working knowledge of SQL, Python and Power BI and exposure to PySpark, Databricks and SAS.

Retail Assets domain knowledge and exposure to Advanced Analytics, Machine Learning and GenAI solutions would be preferred.

Desired Behavioral / Functional Traits

• Strong Retail Banking business understanding
• Understanding of Secured and Unsecured Retail Asset Products.
• Understanding of predictive modelling and machine learning applications in banking analytics.
• Strong working knowledge of SQL, Python and PySpark
• Hands-on exposure to Databricks, SAS and Power BI
• Knowledge of Medallion Architecture and Data Lakehouse
• Understanding of Kimball methodology and Star Schema
• Strong stakeholder management and team leadership capabilities