What You Will Do:
- Analyze customer, transactional, behavioral, and product data to identify trends and business opportunities.
- Perform exploratory data analysis (EDA) and generate actionable insights.
- Develop dashboards and reports to support business decision making.
- Translate business problems into analytical solutions.
Machine Learning and AI Development
- Build, validate, and deploy machine learning models for:
- Propensity Modeling
- Customer Segmentation
- Churn Prediction
- Next Best Action (NBA)
- Cross-Sell/Upsell Recommendations
- Fraud Detection
- Credit Risk Modeling
- Collections & Recovery Analytics
- Apply statistical and machine learning techniques to solve business problems.
- Monitor model performance and recommend enhancements.
Data Preparation and Engineering
- Extract, cleanse, transform, and integrate data from multiple sources.
- Create reusable feature engineering pipelines.
- Ensure data quality, consistency, and governance standards are maintained.
- Work closely with Data Engineering teams on data sourcing and pipeline development.
Model Governance
- Prepare model development documentation and validation packs.
- Support model governance requirements and regulatory compliance.
- Maintain model inventories and monitoring reports.
- Assist during internal audit and model validation exercises.
Stakeholder Management:
- Collaborate with business teams to understand requirements.
- Present analytical findings in a clear and business-friendly manner.
- Participate in project discussions and workshops.
Innovation and Research:
- Continuously evaluate emerging AI/ML techniques.
- Conduct Proof of Concepts (PoCs) for new use cases.
- Identify opportunities for automation and advanced analytics adoption.