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Remote hiring, mid-level Data Scientist title, and broad ML requirements increase candidate density.
Core ML and production skills transfer across industries, but marketplace, RL, and conversational expertise increase specificity.
Explicit 3–4 years plus required ML/causal/RL/NLP production skills and Python/SQL create stringent filters.
Build, deploy, and maintain machine learning and optimization models for quick-commerce marketplace features including supply-demand matching, surge pricing, recommendations, and ETA prediction.
Design and execute experiments (A/B and quasi-experimental) to inform data-driven decisions in pricing, matching, and conversational AI.
Own the full lifecycle of ML models including feature engineering, production deployment, monitoring, and iterative improvements.
3 to 4 years of professional data science or machine learning experience in fast-paced product startups or large tech enterprises.
Strong expertise in A/B test design and causal inference methods including diff-in-diff and instrumental variables.
Proficiency in production ML engineering: deploying, monitoring, and maintaining ML pipelines.
Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or equivalent quantitative field.
Experience in quick commerce, marketplaces, logistics, ride-hailing, or on-demand delivery with knowledge of two-sided supply/demand dynamics.
Hands-on expertise in NLP or large language model techniques relevant to conversational AI, including intent classification and entity extraction.
Comfortable with big-data streaming technologies (e.g., Spark, Kafka) and real-time ML inference, and able to treat models as products across stakeholder groups.