Data Scientist

About the Role / About the Job
In this role, you will leverage data analytics and machine learning to drive business insights and decision-making. You will analyze customer, transactional, behavioral, and product data to identify trends and opportunities within the organization. Your work will involve generating actionable insights through exploratory data analysis, developing dashboards, and translating business problems into effective analytical solutions.

Key Responsibilities

• Data Analysis

  • 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.