Data Scientist III – Meesho

Opportunity Overview

Data Science Lifecycle & Platform Workflow

The Data Scientist III addresses core e-commerce challenges—including supply chain SLA tracking, reseller monetization, personalized recommendations, and seasonal demand forecasting:

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│ 1. Data Aggregation & Extraction         │ ➔ SQL, Distributed Systems (Spark, Redshift), Transaction logs
└─────────────────────┬─────────────────────┘
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┌───────────────────────────────────────────┐
│ 2. Predictive & ML Model Development     │ ➔ Reseller preference mapping, demand forecasting, discount optimization
└─────────────────────┬─────────────────────┘
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┌───────────────────────────────────────────┐
│ 3. Experimentation & Hypothesis Testing   │ ➔ A/B testing design, statistical validation, variance mitigation
└─────────────────────┬─────────────────────┘
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┌───────────────────────────────────────────┐
│ 4. Product Integration & Leadership       │ ➔ Supplier SLA bottleneck removal, cross-functional strategy, mentoring
└───────────────────────────────────────────┘

Key Responsibilities

• Reseller & Customer Personalization: Construct machine learning models to map reseller preferences, optimize product discovery, and improve end-customer revenue loops.

• Pricing & Discount Optimization: Design, model, and evaluate dynamic discount programs and incentive structures to maximize reseller transaction volumes.

• Demand Forecasting & Supply Chain SLAs: Model seasonal demand surges to predict organisational KPIs and analyze logistics data to resolve supplier SLA bottlenecks.

• A/B Testing & Statistical Rigor: Design and analyze experimentation frameworks (A/B tests) to evaluate product features while preventing common model evaluation errors.

• Mentorship & Cross-Functional Alignment: Mentor junior data scientists and translate complex predictive outputs into strategic insights for business, product, and tech leadership.

Technical & Qualification Requirements

Baseline Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related quantitative field.

• Experience Range: 4 to 7 years of hands-on data science experience, ideally within a B2C e-commerce or product-led tech ecosystem.

• Technical Skills: Proficiency in Python, R, and SQL, along with strong foundations in Applied Statistics, Linear Algebra, Machine Learning, and Neural Networks.

Preferred Technical Skill Matrix

Application & Assessment Preparation

1. E-Commerce Machine Learning Scenarios: Prepare to discuss system design for personalization, search ranking, dynamic discount modeling, and churn prediction in high-volume retail apps.

2. Experimentation Rigor: Review experimental design concepts, including sample size estimation, variance reduction techniques, handling network effects, and identifying model overfitting.

3. Big Data & SQL Proficiency: Practice writing complex SQL queries and explaining PySpark/Spark data transformations used for large-scale transaction datasets.