Jobs at Relay Technologies

Own Relay’s sourcing function by managing AI-powered sourcing agents, building talent pools and market maps, growing a talent community, driving employee referrals, and measuring sourcing effectiveness. The role requires proactive pipeline development, market intelligence, candidate engagement, events, content, stakeholder partnership, and experimentation with new sourcing tools and approaches.

Own marketplace decision-making across booking, matching, pricing, and driver incentives. Identify problems, design decision frameworks, analyze experiments, build data applications and monitoring tools, and influence squad priorities. The role requires hands-on SQL, causal reasoning when clean experiments are unavailable, close investigation of real-world logistics operations, and ownership of measurable outcomes in a fast-changing two-sided marketplace.

Own marketplace decision-making across booking, matching, shifts, and pay. Identify high-impact problems, define decision systems, analyze experiments, build data applications, monitors, and alerts, and influence squad priorities. The role requires hands-on SQL analysis, investigation of driver and courier behavior, rapid iteration, and close engagement with real-world logistics operations. Decision Scientists partner with engineering and operational research to measure outcomes and improve Relay’s two-sided delivery marketplace.

Owns fraud, risk, and trust decisions for Relay’s delivery-partner network. The role investigates operational signals, defines evidence and rules, directs product, policy, or training interventions, builds SQL-based data applications and monitoring tools, and validates results in the field. The Staff Decision Scientist partners with engineering and operations to improve parcel handovers, detect delivery failures, establish network-wide standards, and ensure analysis drives measurable changes.

Own Sales decision-making by determining client and shop value, prioritizing deals, forecasting quarterly performance, and defining commercial metrics. Design and build data applications, monitoring, alerts, and valuation methods using SQL, Claude, and AI agents. Partner with sales and data teams to improve data quality, investigate discrepancies in physical operations, and measure whether interventions work. The role requires hands-on commercial analytics, strong ownership, and influencing leaders to change sales strategy.

Owns end-to-end decision-making for partner excellence, including identifying operational problems, evaluating fraud and trust signals, defining rules and standards, and directing product, policy, or training interventions. The role builds data applications, monitors, and alerts; investigates discrepancies between data and physical operations; ensures missing records are created; and measures whether fixes work across Relay’s delivery network. Strong SQL skills and experience operationalizing live risk or trust decisions are required.

Own commercial decision-making for Sales, including customer and shop valuation, pricing, deal prioritization, forecasting, and sales effectiveness. Design signals, metrics, data applications, monitoring, and alerts; collaborate with engineering and analytics teams to build missing data; investigate discrepancies between operational reality and analysis; and influence sales leaders to change strategy based on measurable results.

Lead optimization of Relay’s last-mile delivery network, developing production-scale solvers and algorithms for routing, pricing, and scheduling under uncertainty. Integrate machine learning and recommender systems with operational research, collaborate with scientists, and drive research quality, team growth, and technical thought leadership.

Lead the design, productionization, and evolution of Relay’s large-scale recommender systems. Build models that balance courier preferences, network constraints, pricing, and operational optimization. Partner with operational research teams, guide recommender-system strategy, contribute to research culture, and communicate complex technical ideas clearly. The role requires deep expertise in recommender systems, reinforcement learning, and LLMs, along with experience owning production systems and leading technical direction.

Owns B2B sales and unit-economics decisions, including customer and shop valuation, pricing analysis, lifetime value, deal economics, forecasting, and commercial prioritization. The role designs decision frameworks, builds data applications, monitors, and alerts using SQL and AI tools, improves data quality, investigates operational discrepancies, and proves the impact of changes on sales outcomes and quarterly targets.

Lead the development of Relay’s large-scale recommender systems for courier-route matching. This role owns recommender evolution from research to production, integrates machine learning models with operational research and network optimisation, designs experiments, and shapes the company’s research culture and technical direction.

Own marketplace decision-making across booking, matching, pricing, and driver shifts. Identify real problems, define success metrics, guide product changes, build data applications, monitors, and alerts, and evaluate experiments. The role partners with engineering, data science, and operations, investigates discrepancies in real-world logistics, develops missing data capabilities, and drives measurable improvements in a two-sided marketplace.

Own fraud and Partner Excellence strategy across Relay’s delivery network. Investigate fraud, risk, and trust signals; define rules, evidence standards, monitoring, and interventions; and turn analysis into product, policy, or training decisions. Build data applications, alerts, and operational tools using SQL, Claude, and AI agents. Work directly with physical operations to resolve data gaps, validate fixes, and measure outcomes across a high-volume live network.

Own Marketplace decision-making across booking, matching, pricing, and driver shifts. Identify meaningful problems, design metrics and decision systems, analyze experiments, build data applications and monitoring tools, and influence product and engineering priorities. The role requires hands-on SQL analysis, understanding marketplace incentives and user behavior, validating changes in real-world operations, and rapidly shipping practical solutions.

Lead causal inference and experimentation for Relay’s two-sided logistics marketplace. Measure the effects of operational decisions on couriers over time, adapt advanced causal inference, statistical, and machine learning methods, and bring models into production. Partner with Operational Research to integrate insights into network-wide optimization while shaping experimentation standards and contributing to Relay’s research culture.

Own B2B pricing and commercial decision-making by determining customer and shop value, designing sales metrics, building data applications and monitoring tools, improving data collection, and assessing whether sales targets will be achieved. The role requires hands-on SQL work, stakeholder influence, experimentation, and translating analysis into decisions that shape sales strategy.

Own courier pricing, pay, and incentive policies across Relay’s delivery marketplaces. Apply economic theory, econometrics, and causal inference to model supply, demand, and courier behavior; evaluate pricing changes through experimentation; and partner with operational research and recommender systems teams. Advise leadership on pricing decisions, build hands-on models and analyses, and communicate findings clearly to operations, product, and executive stakeholders.

Own courier pricing, pay policies, and incentive mechanisms across Relay’s delivery marketplaces. Apply economic theory, econometrics, and causal inference to model supply, demand, and courier behavior; measure pricing changes through experimentation; and collaborate with operational research and recommender systems teams. Advise leadership on pricing decisions, build hands-on models and analyses, and contribute to Relay’s research culture and external voice.

Own commercial and unit-economic decision-making for sales, clients, and network partners. Define customer and deal valuation methods, identify meaningful sales problems, build data applications, monitoring, and alerts, and influence sales strategy. Establish data quality, forecast quarterly performance, validate changes, and ensure insights drive business decisions. The role requires hands-on SQL analysis, practical tool building, and close collaboration with sales, analytics engineering, operations research, and data science teams.

Own B2B pricing and commercial decision-making by determining customer and shop value, designing sales signals and metrics, forecasting quarterly performance, and building data applications, monitors, and alerts. The role requires hands-on SQL analysis, cross-functional data development, field validation, and influencing sales strategy through measurable insights and shipped tools.