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Think41 - Data Analyst - Python

Think41 - Data Analyst - Python

Think41Bangalore
18 days ago
Job description

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 audits
  • Cloud 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)

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