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Remote mid-level ML role with popular title and broad requirements increases applicant density.
Strong ML/AI and production requirements make skills transferable but marketplace experience preferred.
Explicit 3–4 year requirement plus mandatory causal inference, RL, NLP, and production ML skills raises filter strictness.
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Develop and maintain machine learning and optimization models for quick-commerce marketplace functions such as supply-demand matching, pricing, recommendations, and ETA prediction.
Design and execute experiments (A/B tests and quasi-experiments) to analyze and optimize marketplace features, translating data insights into actionable decisions.
Own end-to-end lifecycle management of models including feature engineering, deployment, monitoring, and iterative improvements in collaboration with cross-functional teams.
3 to 4 years of professional data science or machine learning experience in startups or high-scale tech enterprises.
Solid experience in experiment design, causal inference methods, and understanding marketplace/network interference effects.
Strong skills in forecasting, operations research or reinforcement learning related to allocation, matching, or pricing problems.
Proficiency in Python and SQL, with experience deploying and maintaining production ML pipelines; Bachelor's/Master's degree in a quantitative field.
Experienced in quick-commerce, marketplaces, logistics, ride-hailing, or on-demand delivery domains with knowledge of two-sided supply/demand dynamics.
Hands-on expertise in NLP or large language models for conversational AI, intent classification, entity extraction, or voice data relevant to the Butler assistant.
Comfortable working with streaming/big-data technologies (e.g. Spark, Kafka) for real-time inference and capable of treating models as live products with cross-team collaboration.