---
title: "Anubhav Saxena - Product Analyst in Aonla | Tal"
description: "Meet Anubhav Saxena, Product Analyst | Data Analyst | SQL | Python | smallcase| Fintech | Visualization | Trend Analysis | Metabase | Superset | Spark SQL | 3+ Years E-commerce & Analytics | SQL, Python| based in Aonla. AI stack includes claude code, claude, and codex."
image: "https://tal.club/meet/anubhav-saxena-1/share-image"
---

# Anubhav Saxena on Tal

## Headline

Product Analyst \| Data Analyst \| SQL \| Python \| smallcase\| Fintech \| Visualization \| Trend Analysis \| Metabase \| Superset \| Spark SQL \| 3\+ Years E\-commerce & Analytics \| SQL, Python\|

## About

As a results\-driven Data Analyst with over 2\+ years of experience in e\-commerce and analytics, I specialize in leveraging data to drive strategic decision\-making and operational efficiency\. My expertise in SQL, Python, and geo\-spatial analysis has enabled me to significantly reduce Return\-to\-Origin rates and improve delivery performance\. Passionate about transforming complex data into actionable insights, I thrive in dynamic environments where data\-driven solutions are key to success\. At Meesho, I played a pivotal role in identifying high\-risk customer cohorts and optimizing logistics operations, demonstrating my ability to enhance business outcomes through innovative data strategies\.

## Location

Aonla

## AI stack

claude code

claude

codex

cursor

gemini

n8n

## Work experience

Product Analyst at smallcase, Jun 2026 \- Present · 5 mos, Bengaluru, Karnataka, India

Data Analyst at Meesho, Apr 2025 \- Jun 2026 · 1 yr 3 mos, Bengaluru, Karnataka, India, Grocery\(Analytics\) Business Analyst at Meesho 2025\-04 \- Present – Part of a cross\-functional growth initiative targeting 4x grocery DAU growth built end\-to\-end funnel teardown across paid acquisition channels, uncovering only 30% targeting accuracy and 57% Meta redirection failure, with independently sized fixes projecting recovery of approximately 1,750 DAUs\. – Segmented high\-risk customer cohorts using IQR and percentile\-based thresholding; identified around 100 customers with more than 90% return behaviour and transitioned them to prepaid\-only, reducing pilot RTO by approximately 0\.5–0\.6 percentage points\. – Improved geo\-coverage for zero\-order\-delivered customers by integrating internal UID\-based lat\-long model outputs, increasing usable location accuracy by nearly 70% for downstream operational and targeting analysis\. – Built SQL\- and Superset\-based dashboards along with automated reporting workflows for growth and operations tracking, eliminating repetitive MIS work and saving approximately 4–6 hours of manual effort weekly\. – Performed geo\-spatial analysis on order\-level lat\-long datasets to identify high\-RTO micro\-clusters hidden within broader pincodes, enabling more precise operational interventions and root\-cause diagnosis\. – Analysed large\-scale marketplace datasets across customer, order, return, and delivery metrics to uncover anomalies, conversion leakages, and operational inefficiencies impacting growth and revenue performance\. – Validated week\-on\-week trends and behavioural shifts to eliminate temporal bias caused by festival spikes and short\-term anomalies, ensuring only structurally problematic zones were actioned\. – Collaborated with supply chain, operations, and field teams to validate root causes such as rider issues, infrastructure gaps, and customer behaviour patterns, translating findings into actionable business recommendations\.

Data Scientist at QBS Learning, Sep 2024 \- Jan 2025 · 5 mos, Data Scientist at QBS Learning 2024\-09 \- 2024\-12 \- Integrated OpenAI APIs to automate educational content creation, enhancing production efficiency\. \- Developed prompt engineering frameworks, aligning generated content with curriculum standards\.

Data Analyst at Learnbay, Feb 2024 \- Sep 2024 · 8 mos, On\-site, \*\*Data Cleaning and Transformation\*\*: \- Identify and handle missing values, outliers, and inconsistencies\. \- Standardize data formats and resolve data quality issues\. \*\*Insight Generation\*\*: \- Analyze visualizations to identify patterns, trends, and correlations in the data\. \- Extract actionable insights and key findings from the visualizations\.

Human Resources Associate at Cyfuture, Apr 2023 \- Feb 2024 · 11 mos, Noida, Uttar Pradesh, India, Sourcing and Talent Identification

## Education

Master's degree, Business Administration and Management, General, New Delhi institute of Management

[View the original profile](https://tal.club/meet/anubhav-saxena-1)

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