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).
