Location : Think41, HSR layout Bangalore
Job Type : Full-Time
About Think41 :
Think41 is an early-stage startup with a mission to build a leading technology consulting firm providing Gen AI Services & Solutions. Our leadership team previously built HashedIn, a cloud services & solutions company acquired by Deloitte in 2021, now a cornerstone of Deloitte Engineering. With early funding established, Think41 is looking to assemble an all-star engineering team passionate about Gen AI tools and ecosystems.
Role summary :
Support day-to-day retail analytics across sales, margin, assortment, pricing / promotions, inventory and returns. Build reliable dashboards, write clean SQL / Python, run ad-hoc deep dives, and help evaluate experimentspartnering closely with business, product, and :
- Build and maintain executive and team dashboards (Power BI / Tableau / Looker Studio); automate recurring reports; document metric definitions.
- Write performant SQL (joins, window functions, CTEs) and use Python (pandas / NumPy) for wrangling and QC; publish reusable queries / views.
- Run deep dives on price & promo performance, category / basket KPI shifts, stockouts / returns, conversion funnels and cohorts; recommend next actions.
- Assist with A / B and holdout analysesdefine metrics, sanity checks, readouts, and learnings.
- Partner with product / marketing on GA4 / GTM tracking; validate tags and reconcile web / app data with warehouse tables.
- Monitor data freshness / accuracy, raise issues, and maintain playbooks / SOPs.
- Under guidance, review low-risk AI-agent suggestions (e.g., minor promo tweaks, exception queues), record rationale, and escalate edge cases.
Must-have qualifications :
23 years in analytics / BI; retail / e-commerce / CPG exposure preferred.Strong SQL; solid Excel; experience with at least one BI tool (Power BI / Tableau / Looker Studio).Working Python (pandas / NumPy) for data prep and analysis; Git basics helpful.Comfort with funnels / cohorts, conversion / AOV, margin basics, and interpreting KPI movements in a retail context.Fundamentals of experimentation and descriptive statistics; ability to explain assumptions / limitations.Clear communication : concise summaries, structured storytelling, and stakeholder management.Nice-to-have (aspirational, not mandatory) :
GA4 / GTM implementation auditsCloud DW exposure (BigQuery / Snowflake / Redshift) and basic dbt / Airflow concepts.Forecasting / promo-uplift basics (Prophet / ARIMA) and simple optimization intuition (assortment / inventory).Experience with QA of tracking, metric governance, and lightweight scripting for automation.(ref : hirist.tech)