Data Analyst Intern (AI Science Team)

**Responsibilities** **About Aisensum** We build agentic AI teammates, custom AI agents like ROI Daniel, ROT Sasha, and ROQ Nadia that eliminate grunt work, boost efficiency **Who We Want** A fresh grad or final-year student (preferably Economics / Econometrics / Statistics / Mathematics / Data Science) who is strong on business thinking, comfortable with SQL & Python, and can translate messy data into decisions that affect revenue, margins, or productivity. Work on real client projects: turn raw transaction/CRM/kiosk data into actionable models, dashboards, and recommendations that drive measurable business outcomes. **Requirements** **Key Responsibilities** * Clean, merge, and QA multi-source datasets (CRM, POS, kiosks, Excel extracts). * Write & optimise SQL (joins, aggregations, window functions). * Descriptive analytics: cohorts, funnels, RFM/RFMP, conversion funnels. * Build and validate logistic regression/propensity models; interpret results for business owners. * Produce management-ready charts, short slide summaries, and one-line recommendations. * Support Senior Ops/AI Science in data checks, UAT, and deployment monitoring. * Document assumptions, edge-cases, and data quality issues. **Must-have Skills** * Degree: Economics / Econometrics / Statistics / Math / Data Science (or equivalent). * SQL: basic → intermediate (joins, group by, filters; window functions preferred). * Python: Pandas / NumPy for data cleaning & analysis. * Statistics: regression (logistic), hypothesis testing, distributions, sampling. * Business sense: can answer — “what does this number mean for revenue, cost, or quality?” * Clear written English and attention to detail. **Nice-to-have** * Experience with BI tools (Tableau / Power BI / Looker Studio). * Coursework or projects in propensity modelling, pricing, and demand forecasting. * Basic familiarity with Git or other version control systems. * Exposure to real-world datasets (through internships, competitions, and capstone projects). **What You’ll Learn** * Running end-to-end analytics for paying clients (not toy datasets). * Turning models into decisions that affect ROI / ROT / ROQ. * Exposure to deploying AI teammates (Daniel / Sasha / Nadia use-cases).