Data Scientist II

Mrsool

IndiaremotePosted 1 month ago

Skill Required

Data-ScienceMachine-LearningAnalyticsTechnologyIT-ServicesEngineeringData-Scientist-IIData ScientistObservabilityStatisticsKafkaPythondesignDevOpsSparkDesign PatternsNLPSQLandGenerative AIAIMachine LearningFulltime

Key highlights

3 to 4 years of professional experience required.
Competitive compensation including potential share options.
Role offers remote work environment.
Focus on quick-commerce and marketplace optimization.
Annual learning stipend provided.
Ownership of end-to-end model lifecycle.

Role overview

Mrsool is a leading on-demand delivery platform in the MENA region, providing an 'order anything from anywhere' experience. We are seeking a Data Scientist II (DS-2) to join our core data science team, where you will build and ship end-to-end models that power our quick-commerce marketplace, from analysis and experimentation to production.

Responsibilities

Build and maintain ML and optimisation models across the quick-commerce stack, including supply-demand matching, dynamic/surge pricing, recommendations, ETA prediction, and marketplace optimisation.
Contribute to the AI behind Butler by modelling customer intent from unstructured text, voice, and image requests.
Design and run A/B and quasi-experimental tests across pricing, matching, recommendations, and Butler.
Turn noisy marketplace data into actionable decisions for stakeholders.
Engineer high-signal features from real-world data such as order events, courier traces, geospatial signals, pricing configs, and conversational text/voice.
Own model lifecycles, including data pipelines, training, deployment, monitoring, retraining, and responding to model/config drift.
Collaborate with product managers, engineers, DevOps, and operations teams to deliver data-driven experiences and diagnose live issues.
Proactively monitor model and metric health, instrument work with logging and observability, and contribute to reliable, repeatable analysis and deployment practices.
Identify opportunities to improve measurement, modelling, and process, favoring small, incremental changes.

Requirements

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 (diff-in-diff, instrumental variables, synthetic control).
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.
Proven ability to build, select, and maintain features from large, messy, real-world data.
Comfortable deploying, monitoring, and maintaining ML pipelines.
Engineering discipline to keep models reliable in production.
Fluent in Python and SQL with the ability to work efficiently against large-scale data.
Track record of thinking from first principles and balancing trade-offs like reliability, latency, and interpretability.
Bias towards shipping early and iterating.
Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or an equivalent quantitative field.

Nice to have

Hands-on experience in quick commerce, marketplaces, logistics, ride-hailing, or on-demand delivery.
Understanding of two-sided supply/demand dynamics.
NLP/LLM experience, including intent classification, entity extraction, embeddings, or conversational/voice data.
Experience with streaming/big-data tooling (Spark, Kafka) and real-time inference.
High-agency individuals who treat models as products and collaborate well across conflicting perspectives.