Senior Data Analyst - Operations & Logistics

Overview

In this position, you will work and collaborate with operations managers, fleet and dispatch teams, data scientists, backend engineers, and business stakeholders in a cross-functional setting. Your core function as an Operations & Logistics Data Analyst is to turn delivery and logistics data into actionable insight that improves how orders move from merchant to customer. You will own the metrics that define operational health, such as delivery time, on-time rate, courier utilization, dispatch efficiency, and cost per delivery, and you will use them to diagnose bottlenecks, forecast demand, balance courier supply against order volume, and recommend changes that make the last mile faster, cheaper, and more reliable.

• Collaborate with operations, fleet, dispatch, product, and engineering teams to implement data-driven solutions that improve delivery speed, reliability, and cost.
• Define, monitor, and report on the core operational KPIs, including delivery time, on-time rate, courier utilization, idle time, dispatch and assignment efficiency, and cost per delivery.
• Analyze and interpret complex operational data sets using statistical techniques and provide stakeholders with clear, actionable insight.
• Investigate operational bottlenecks across the order lifecycle (acceptance, preparation, pickup, transit, drop-off) and recommend targeted improvements.
• Build demand forecasts and support capacity planning so courier supply is matched to order volume across zones and time windows, including peak periods.
• Analyze geographic and zone-level performance to inform coverage, courier allocation, and last-mile strategy.
• Design and maintain dashboards and visualizations that give operations teams real-time and historical visibility into performance.
• Stay up to date with industry trends in logistics analytics, dispatch optimization, and last-mile delivery to keep our capabilities competitive.
• Identify areas of opportunity, guide teams in designing complex operational features, and mentor junior analysts in a collaborative environment.

• A bachelor s or master s degree in a related field, such as data science, statistics, mathematics, industrial engineering, operations research, supply chain, or computer science.
• At least 5 years of experience in operations, logistics, or supply chain analytics, preferably in a fast-paced delivery, e-commerce, or marketplace environment.
• Strong proficiency in SQL, Python, and/or R, as well as experience with data visualization tools such as Tableau or Power BI.
• Experience working with geospatial and time-series data (e.g., zones, routes, ETAs, demand by hour and location) and comfort with mapping or spatial analysis.
• Excellent analytical and problem-solving skills, with the ability to extract insight from large, messy operational data sets.
• Effective communication skills, with the ability to translate operational data into clear recommendations for non-technical stakeholders such as fleet, dispatch, and city operations teams.
• Strong attention to detail and accuracy, with the ability to identify and correct errors in data.
• Demonstrated ability to work independently, prioritize tasks, and meet deadlines in a fast-paced, real-time operational environment.
• Strong collaboration skills, with the ability to work effectively within a diverse, cross-functional team.

• Proven experience in food, grocery, or parcel delivery services.
• Familiarity with dispatch, routing, and batching logic, and with the levers that drive last-mile efficiency.
• Demonstrated causal analytics skills or a full understanding of causality (e.g., measuring the true impact of an operational change rather than correlation).
• Experience modeling delivery unit economics at the transaction level (cost per order, courier payout, subsidy, delivery margin) and aggregating it reliably across time, cohorts, zones, and other dimensions.
• Familiarity with demand forecasting, capacity planning, and supply-demand balancing for a courier fleet.
• Exposure to optimization or operations research concepts (assignment, routing, queuing) and to data processing pipelines and data engineering basics.
• Familiarity with machine learning techniques and experience contributing to predictive models such as ETA prediction or demand forecasting.