Data Scientist II

MRSOOL | مرسول

. Build and maintain ML and optimisation models across the quick-commerce stack, including supply-demand matching, dynamic and surge pricing, recommendations, ETA prediction, and marketplace optimisation .

Posted 9/17/2026full-timeRemote • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining machine learning models and optimization techniques, with a strong focus on A/B testing, data pipeline management, and feature engineering from complex datasets. Proficient in Python and SQL, with hands-on experience in quick commerce and marketplace dynamics.

Highest-signal resume keywords

Machine Learning Model DevelopmentA/B Test DesignFeature EngineeringPython ProgrammingSQL Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningOptimization ModelsA/B TestingForecastingOperations ResearchReinforcement LearningFeature SelectionData Pipeline ManagementNLP/LLMIntent Classification

Soft Skills

First-Principles Problem-SolvingIterative Development

Tools & Technologies

SparkKafkaBig Data ToolingReal-Time Inference

Industry Keywords

Quick CommerceMarketplacesLogisticsRide-HailingOn-Demand Delivery

Tech Stack

Tools & technologies

KafkaPythonSparkSQL

About the role

Key responsibilities & impact

Build and maintain ML and optimisation models across the quick-commerce stack, including supply-demand matching, dynamic and surge pricing, recommendations, ETA prediction, and marketplace optimisation
Contribute to Butler's conversational ordering AI by modelling customer intent from text, voice, and images and mapping requests to fulfillable, well-priced orders
Design and run A/B and quasi-experiments across pricing, matching, recommendations, and Butler
Turn noisy marketplace data into actionable stakeholder decisions
Engineer features from order events, courier traces, geospatial signals, pricing configurations, and conversational text/voice
Own models through data pipelines, training, deployment, monitoring, and retraining
Respond to model or configuration drift
Collaborate with product managers, engineers, DevOps, operations, and other squads
Help diagnose live issues such as mispriced brackets and elevated failure rates
Monitor model and metric health and contribute to logging, observability, analysis, and deployment practices
Identify improvements to measurement, modelling, and processes through incremental changes

Requirements

What you’ll need

3 to 4 years of non-internship professional data science or ML experience in fast-paced product startups or high-scale tech enterprises
Solid command of A/B test design, power analysis, and quasi-experimental methods, including diff-in-diff, instrumental variables, synthetic control, and awareness of interference in marketplace/network settings
Strong grounding in forecasting and at least one of operations research or reinforcement learning applied to allocation, matching, or pricing problems
Proven ability to build, select, and maintain features from large, messy, real-world data
Comfortable deploying, monitoring, and maintaining ML pipelines
Fluent in Python and SQL
Ability to work efficiently against large-scale data
First-principles problem-solving and track record of delivering high-quality work while balancing reliability, latency, and interpretability
Bias toward shipping early and iterating
Bachelor's/Master's degree in Computer Science, Statistics, Engineering, or an equivalent quantitative field
Hands-on experience in quick commerce, marketplaces, logistics, ride-hailing, or on-demand delivery
Experience with NLP/LLM, including intent classification, entity extraction, embeddings, or conversational/voice data
Comfortable with streaming/big-data tooling such as Spark and Kafka and real-time inference

Benefits

Comp & perks

Inclusive and diverse workplace
Remote work environment
Competitive compensation
Potential share options for certain roles
Regular training
Annual learning stipend
High degree of autonomy
Mentorship