Data Analyst Intern (AI Science Team)

Tanggung jawab

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.

Persyaratan

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).