Data Scientist II

Who Are We❓ Welcome to the world of Mrsool! 🌍✨ Where on-demand delivery meets unparalleled user needs to deliver anything you desire. As one of the largest delivery platforms in the Middle East and North Africa (MENA) region, Mrsool has captivated users with its unique and seamless experience, earning it the highest ratings among all major delivery platforms on both Apple's App Store and Google's Play Store. 🌟📲 What sets Mrsool apart is its commitment to providing an unmatched "order anything from anywhere" experience. 🌐📦 This extraordinary feat is made possible by our extensive fleet of dedicated on-demand couriers. With their unwavering dedication, they ensure that your desired items reach your doorstep, no matter where you are. 🚗🚲 Whether it's a late-night craving, a forgotten item, or a special gift for a loved one, Mrsool is here to deliver, quite literally. 😋🎁 We take pride in the convenience we offer, empowering you to get what you need when you need it, all at the tap of a button. 💪🏼💫 The Job in a Nutshell💡 We are seeking a Data Scientist II (DS-2) to join our core data science team. In this role, you will build and ship models that power Mrsool's quick-commerce marketplace, owning well-scoped problems end-to-end — from analysis and experimentation through to production. You will work closely with cross-functional teams and senior data scientists to deliver robust, data-driven solutions. This position offers an opportunity to grow your craft on high-impact problems and contribute directly to the growth and success of the organization. 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 w