Data Scientist – Card Products & Loyalty

Job Description

We are looking for a Data Scientist with a strong background in predictive modeling, customer segmentation, and loyalty analytics related to card products (credit/debit). You will work closely with cross-functional teams to develop data-driven solutions that enhance card usage, optimize reward programs, and support strategic initiatives such as cross-sell, retention, and churn prevention. You will be responsible for building models, validating hypotheses, and turning complex data into actionable insights that drive business impact.

• Develop and maintain machine learning models for use cases such as churn prediction, credit scoring, card usage forecasting, cross-sell propensity, and campaign personalization etc.
• Conduct customer segmentation using clustering, RFM, and other statistical methods.
• Analyze card transactions, spending patterns, and loyalty behavior to detect trends and risks.
• Collaborate with Product, Marketing, and CRM teams to design and evaluate A/B tests and targeting strategies.
• Build data pipelines and monitor model performance post-deployment.
• Translate complex analytical results into business-friendly insights.
• Contribute to model governance, data quality, and responsible AI practices.

Job Requirement

Required Qualifications:

• 3+ years of experience in Data Science, preferably in Banking, Fintech, or Loyalty.
• Proficient in Python and SQL for data analysis and modeling.
• Strong knowledge of machine learning libraries (e.g., scikit-learn, XGBoost, LightGBM).
• Hands-on experience with data warehouses (Snowflake, Redshift, S3) and cloud platforms (AWS preferred).
• Familiarity with lifecycle management of models in Amazon SageMaker.
• Good understanding of customer lifecycle, card usage, and financial products.
• Strong communication and storytelling skills, both written and verbal.

Nice to Have:

• Experience integrating models into APIs or CEP platforms like MoEngage.
• Knowledge of attribution modeling, uplift modeling, or time-series forecasting.
• Familiarity with orchestration and automation tools (e.g., Airflow, MLflow).
• Experience in mentoring junior team members or working within data squads.

BENEFITS:

• Competitive salary package (Base salary and performance bonuses).
• Probation period salary is 100% of the official salary.
• Comprehensive health and accident insurance.
• 15 days of annual leave.
• Provision of work equipment (Macbook/ Laptop, mouse, monitor, etc.).
• A creative and modern working environment.