Think41 - Data Analyst - Python

6 days ago

Bengaluru, Karnataka, India THINK41 TECHNOLOGIES PRIVATE LIMITED Full time ₹ 9,00,000 - ₹ 12,00,000 per year

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 engineering.

Responsibilities :

- 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 :

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.

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