Data Scientist

Role & responsibilities

• Develop and implement statistical models and machine learning algorithms for customer behavior prediction, and product optimization.
• Analyze large-scale structured and unstructured data using Python, SQL, Databricks and other data tools.
• Partner with business stakeholders to define problems, determine analytic approaches, and deliver insights and recommendations.
• Conduct deep dives into credit card customer behavior to identify opportunities for acquisition, retention, and cross-sell.
• Design A/B tests and interpret results to guide marketing and product strategies.
• Work with data engineering teams to ensure scalable and production-ready data pipelines.
• Knowledge of SAS is preferred.
• Stay updated on the latest advancements in machine learning and analytics to continuously improve models and methodologies.
• Collaborate with cross-functional teams to ensure alignment between analytics solutions and business objectives.

Preferred candidate profile

Experience:

Technical Proficiency:

• Expertise in Python (pandas, numpy, scikit-learn, etc.) for data analysis and modeling.
• Advanced knowledge of SQL for data extraction, transformation, and manipulation.
• Solid experience in statistical modeling and machine learning techniques (e.g., regression, decision trees, clustering, XGBoost, SAS is preferred).

Domain Knowledge:

• Prior experience in banking or financial services, especially in credit card analytics, is mandatory.
• Strong understanding of business metrics and KPIs in the financial domain.

Additional Skills:

• Ability to design and interpret A/B tests to guide strategic decisions and for campaign management
• Experience with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn) is a plus.
• Excellent communication skills to present technical findings to non-technical stakeholders.

Education:

Bachelor's or masters degree in data science, Statistics, Computer Science, or a related field.