Partner closely with Credit / Retail Finance stakeholders across product, operations, promotion, and collection teams to understand business problems, define analytical approaches, and deliver actionable insights.
Support data analysis and performance tracking across Credit business areas, including user conversion funnels, A/B testing, campaign performance, CRM engagement, collection performance, product journeys, and customer segmentation.
Contribute to BI work for upcoming AI initiatives such as Promotion AI CRM, and Collection AI Chatbot by defining success metrics, experiment frameworks, monitoring dashboards, and post-launch performance reviews.
Consolidate data from multiple sources, including business systems, marketing data, operational data, product behavior logs, and data warehouse tables, to build integrated datasets, reports, and dashboards for decision-making.
Build, maintain, and enhance BI reports, Data Studio dashboards, Google Sheets analysis templates, and management reporting tools with clear metric definitions and reliable refresh logic.
Critically evaluate business metrics and data quality, including metric definitions, date windows, partition logic, join granularity, funnel attribution, duplication risks, and abnormal trends.
Improve internal processes through automation, reusable analysis templates, data validation workflows, and AI-assisted BI workflows to increase team productivity and reporting accuracy.
Collaborate with data engineering, product, and tech teams to improve data marts, tracking design, data pipelines, and scalable analytics solutions.
Requirements
Bachelor's degree or above in Computer Science, Data Science, Statistics, Mathematics, Business Analytics, Information Technology, Finance / Banking, or related fields.
Min. 1 year of experience in data analytics, business intelligence, data product, data engineering, or related roles.
Strong SQL skills, with hands-on ability to work on complex data extraction, joins, aggregations, window functions, funnel analysis, and data validation. Python experience for data analysis or automation is required.
Familiarity with at least one BI or visualization tool, such as Data Studio, Tableau, Power BI, Looker, Superset, or Google Sheets.
Strong business sense and analytical thinking, with the ability to translate business questions into clear metrics, structured analysis, and practical recommendations.
Detail-oriented and data-quality minded, with the ability to proactively validate metric definitions, sample logic, date windows, join granularity, and outliers.
Able to work effectively with cross-functional teams in a fast-paced environment, with strong communication skills and ownership.
Experience in finance, banking, consumer finance, credit, payments, risk, CRM, collection, growth marketing, or A/B testing is a plus.
Experience with AI products, GenAI tools, AI chatbots, intelligent marketing, analytics automation, or LLM-assisted data workflows is a plus.
