Data Science Manager, Courier Pay
Role details
Job location
Tech stack
Job description
As Data Science Manager for Logistics, you'll lead a team building robust, well-engineered, data-driven solutions to exactly these problems. You'll own the vision and execution of data science for your vertical - setting the strategy, raising the technical bar, and developing a team of senior scientists and team leads - while staying close enough to the work to guide hard modelling decisions. Your remit is both deep and broad: translate the toughest logistics challenges into modelling strategies, then hold the team accountable for solutions that move the metrics that matter. This is a high-impact, high-ownership role for someone who wants their team's work to be felt across a global operation every single day. These are some of the key components to the position:
- Lead, coach, and scale a high-performing team of up to 20 Data Scientists, Operations Research Scientists, and Machine Learning Engineers.
- Build a durable leadership bench by mentoring senior individual contributors and team leads toward management and staff-level impact.
- Strategic owner of hiring, workforce planning, and organizational design to structure the team effectively as it scales.
- Foster a high-velocity, intellectually honest culture grounded in experimental rigour, high technical quality, and strong outcome ownership.
- Own the logistics data science roadmap, partnering with cross-functional leadership to identify and prioritize high-leverage business opportunities.
- Translate intricate logistics challenges like network congestion, pricing inefficiencies, and supply shortfalls into robust modeling strategies.
- Represent the vertical on the data leadership team, driving tooling and operational standards that elevate data science across the entire company.
- Collaborate with ML Engineering to architect, deploy, and monitor highly scalable, production-ready machine learning systems.
- Guide technical decisions regarding real-time inference and optimization under uncertainty, maintaining a strict bar for model reliability and maintainability.
- Act as the primary data science voice to VP-level stakeholders, translating complex model behavior into business outcomes that move key logistics metrics.
Requirements
- A degree in a quantitative field: Data Science, Statistics, Operational Research, Mathematics, Computer Science, or similar.
- A proven track record leading high-performing data science teams in a fast-moving product or tech environment, with direct management experience across senior ICs and team leads.
- Deep expertise in machine learning, optimisation, and statistical modelling, with strong intuition for where these techniques create real business leverage.
- Hands-on experience building and deploying ML models in production, ideally in real-time or high-throughput systems. You won't be coding daily, but you need the credibility to guide technical decisions at depth.
- Fluency in causal inference and experimentation, including advanced designs such as switchback testing, difference-in-differences, and marketplace-aware frameworks.
- Strong commercial instincts, connecting modelling decisions to P&L, courier supply health, and customer experience.
- Excellent communication skills, with the ability to influence technical peers and senior business leaders alike.