Data Scientist II

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MRSOOL | مرسول

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2015

🚗 Transport

🛍️ eCommerce

Transport • eCommerce • Logistics

MRSOOL is one of the largest delivery platforms in the region, offering an on-demand experience with high user ratings in both Apple's App Store and Google's Play store. MRSOOL provides a "order anything from anywhere" service backed by a large fleet of registered couriers. It enables businesses to access a vast user base, facilitating transformation to on-demand eCommerce and offering a flexible bidding system for service prices. MRSOOL also offers a personalized delivery experience with real-time tracking and communication with couriers, making it a leading option for ordering from local shops, groceries, and restaurants directly to your door.

📋 Description

• What You Will Do💡 • - Marketplace Modelling: Build and maintain ML and optimisation models across the quick-commerce stack — supply-demand matching, dynamic and surge pricing, recommendations, ETA prediction, and broader marketplace optimisation. • - Butler & Conversational AI: Contribute to the AI behind Butler, Mrsool's distinctive conversational ordering experience — modelling customer intent from free-form, unstructured requests (text, voice, images) and mapping it to fulfillable, well-priced orders. • - Experimentation & Causal Inference: Design and run experiments (A/B and quasi-experimental) across pricing, matching, recommendations, and Butler, and turn noisy marketplace data into decisions stakeholders can act on. • - Feature Engineering & Data Craft: Engineer high-signal features from messy, real-world data — order events, courier traces, geospatial signals, pricing configs, and conversational text/voice — as a core, ongoing part of the role. • - Production ML: Own your models through their lifecycle — data pipelines, training, deployment, monitoring, and retraining — and respond when a model or config drifts. • - Cross-Functional Collaboration: Collaborate effectively with product managers, engineers, DevOps, operations, and other squads to deliver seamless, data-driven experiences and to help diagnose live issues (e.g. mispriced brackets, elevated failure rates in a city). • - Operational Excellence: Proactively monitor model and metric health, instrument your work with proper logging and observability, and contribute to reliable, repeatable analysis and deployment practices. • - Continuous Improvement: Identify opportunities to improve measurement, modelling, and process; favour small, incremental changes that compound over time.

🎯 Requirements

• What Are We Looking For❓ • - Years of Experience: 3 to 4 years of non-internship professional data science or ML experience in fast-paced product startups or high-scale tech enterprises. • - Experimentation & Causal Inference: Solid command of A/B test design, power analysis, and quasi-experimental methods (diff-in-diff, instrumental variables, synthetic control), including awareness of interference in marketplace/network settings. • - ML & Optimisation Depth: Strong grounding in forecasting and at least one of operations research / reinforcement learning applied to allocation, matching, or pricing problems. • - Feature Engineering: Proven ability to build, select, and maintain features from large, messy, real-world data. • - Production Engineering: Comfortable deploying, monitoring, and maintaining ML pipelines, with the engineering discipline to keep models reliable in production. • - Technical Toolkit: Fluent in Python and SQL, with the ability to work efficiently against large-scale data. • - Problem-Solving Mindset: A knack for thinking from first principles and a track record of delivering high-quality work while balancing trade-offs like reliability, latency, and interpretability. • - Iterative Mindset: A bias towards shipping early and iterating; a belief in small, incremental changes over large, multi-quarter undertakings. • - Education: Bachelor's/Master's degree in Computer Science, Statistics, Engineering, or an equivalent quantitative field. • Who Will Excel❓ • - Data scientists with hands-on experience in quick commerce, marketplaces, logistics, ride-hailing, or on-demand delivery, who understand two-sided supply/demand dynamics. • - Those with NLP / LLM experience — intent classification, entity extraction, embeddings, or conversational/voice data — directly relevant to Butler. • - Engineers comfortable with streaming/big-data tooling (Spark, Kafka) and real-time inference. • - High-agency individuals who treat their models as products and collaborate well across conflicting perspectives.

🏖️ Benefits

• **What We Offer You❗** • - Inclusive and Diverse Environment: We foster an inclusive and diverse workplace that values innovation and offers remote environments. • - Competitive Compensation: Our compensation packages are highly competitive and include potential share options for certain roles. • - Personal Growth and Development: We are committed to your personal and professional growth, providing regular training and an annual learning stipend to help you advance your career in a dynamic environment. • - Autonomy and Mentorship: You'll enjoy a high degree of autonomy in your role, supported by mentorship and ambitious goals that pave the way for both your success and the company's growth.