Ahamove is an on-demand logistics platform handling large daily volumes of orders, drivers and delivery points nationwide. Our Data function is split into two groups: Data Platform owns the infrastructure — warehouse, lake, pipelines, analytics engineering — while Data Science & Analytics, the group you will lead, applies that data through models and AI to improve how the platform performs.
You will build the team on an existing foundation, set how it works, and own the data group's project portfolio end to end.
What you will do
- Own the analytics the business runs on: define the metrics that matter, keep them accurate and trusted, and make sure every team — operations, commercial, product, finance — can act on them.
- Turn business questions into analysis, and analysis into decisions: pricing performance, driver supply and utilisation, order economics, customer behaviour, operational cost.
- Build an experimentation culture: design and evaluate A/B tests for pricing, product and operational changes, and make experiment results the standard for how decisions get made.
- Own delivery of the data group's project portfolio: scope, prioritise, and drive projects through to done alongside product, engineering and operations.
- Manage and prioritise the work of the group's data scientists — modeling work such as dynamic pricing and dispatching — making sure it stays tied to measurable business impact, in partnership with technical leads for depth.
- Use analytics insights to surface where deeper modeling or automation is worth investing in next.
- Lead, coach and grow the team; own hiring as it expands.
- Partner with Data Platform on data models, pipelines and quality, and with Backend and Product Owners to ship data products into the platform.
Requirements
- 6+ years in data analytics or business intelligence, including 2+ years managing a team.
- A track record of analytics work that changed real business decisions — not just dashboards delivered, but decisions moved.
- Background in a data-heavy digital product; marketplace, logistics, fintech, e-commerce or ride-hailing preferred.
- Expert SQL on a modern warehouse, and comfort with Python (or equivalent) for analysis.
- Strong statistical foundations: experiment design, hypothesis testing, careful interpretation of results.
- Solid grasp of analytical data modeling and metrics design — enough to set standards for the team and work effectively with analytics engineers.
- Experience with a modern BI stack and building a culture of self-service analytics.
- Communicates complex findings simply and persuasively to non-technical audiences, up to leadership level.
- Prioritises problems by business value, not technical interest.
- Comfortable operating with ambiguity in a fast-moving environment.
Nice to have
- Working knowledge of data science and machine learning — enough to manage data scientists priorities and delivery, ask the right questions of their work, and judge where ML genuinely applies to a logistics business. You will not be building models yourself.
- Familiarity with the model lifecycle at a practical level: how models get to production, how they are measured and monitored.
- Pricing, matching, dispatching or operational optimisation problems.
- Geospatial data, mapping or routing.
- Experimentation platforms or experience running one.
