Data Scientist

Position Overview We are seeking Business Data Analysts to join our in-house data team. The team is building a centralized data platform that consolidates customer data from multiple subsidiary companies with heterogeneous data sources. The primary focus is on deriving customer insights to enable hyper-personalization strategies across business units.

Key Responsibilities 1.Gather and document business requirements from stakeholders across multiple subsidiary companies; conduct workshops and interviews to understand data needs and customer journey touchpoints. 2.Analyze and profile raw customer data from disparate sources to identify data quality issues, inconsistencies, and gaps; propose data harmonization approaches. 3.Design and maintain customer segmentation models and personas using behavioral, transactional, and demographic data to support hyper-personalization initiatives. 4.Develop dashboards and self-service reporting tools (Power BI) that surface customer insights to business users and product teams. 5.Collaborate with Data Engineers to define data transformation rules, business logic, and KPI definitions for the unified data platform. 6.Translate analytical findings into actionable business recommendations presented to senior management. 7.Validate data accuracy post-migration and post-transformation to ensure integrity of consolidated datasets. 8.Monitor and track performance of personalization; measure uplift and iterate on segmentation models. 9.Support end-to-end implementation of data projects including requirement sign-off, UAT coordination, and post-deployment support. 10.Stay current with best practices in customer analytics, data storytelling, and personalization technologies. 11.Development internal AI modelling to fulfill customer segmentation.

Qualifications: Bachelor's degree or higher in Business Analytics, Statistics, Computer Science, Information Technology, or a related field. 3+ years of experience in data analysis, business intelligence, or a related role. Proficiency in SQL for data extraction and analysis; experience with NoSQL databases is a plus. Hands-on experience with BI tools such as Power BI, Tableau, or Looker. Solid understanding of customer segmentation techniques (RFM, clustering, propensity modeling). Experience working with multi-source or multi-entity data environments; ability to reconcile inconsistent data schemas. Strong analytical and problem-solving skills with attention to detail. Good command of English (written and verbal). Experience in financial services, leasing, hire purchase, or personal loan business is a plus. Familiarity with Python or R for data wrangling and statistical analysis is advantageous. Knowledge of Agile/Scrum methodologies and experience working in cross-functional teams. Exposure to machine learning concepts and personalization algorithms. Experience with cloud data platforms (AWS, Azure, GCP) or data lake environments. Familiarity with data governance and data quality frameworks.