Senior Data Analyst

Starship Technologies is revolutionizing deliveries with autonomous robots. These robots are designed to deliver food, groceries, and packages across neighborhoods in minutes. Starship has now completed millions of autonomous deliveries to date, traveled millions of miles, and is currently doing more than 140k road crossings each day. Our contribution to society includes reducing congestion and pollution, providing zero-emissions deliveries, increasing the quality of life for residents, empowering seniors and disabled people, and enabling affordable delivery for local businesses.

We are growing fast, and we are looking for a Senior Data Analyst to join the team.

You will be a key strategic partner to our Commercial and Operational leaders, building the logic that proves the value of our technology and secures both our next level of commercial success and our ability to scale operations to match.

What the job is all about:

The boring part is done. Our data is accessible, the BI layer is solid, and most numbers already exist somewhere. You will not spend your first year digging yourself out of a data hole. What we don't have is enough hands to cover it all as deeply as we'd like. Your work splits in two:

Impacting company strategy - finding answers to questions nobody can answer yet. Should we scale? Where should we scale? At what unit economics? Why does one town work while the next one doesn't? These are “why?” and “what if?” questions, and they make up most of this role.

Freeing up time for the complex questions - making sure we don't have to answer the same questions over and over again. The knowledge mostly exists already, scattered across dashboards and people's heads. Your job is to pull it into the open so that people can answer the easy questions themselves and you can get to the more interesting ones.

Your main responsibilities are:

This role covers two domains that meet in the middle. The Commercial team asks where to expand and once we’re there, how to make it successful. Field Operations asks how we can run it well.

On the commercial side:

• Find the root causes behind business trends to solve complex commercial problems and maximise volume. For example, why the order conversion rate dropped 20% at a site. High ETAs, payment gateway failures, dynamic pricing, school holidays, or an alien invasion?

• Back our expansion decisions with evidence. Which grocery stores or restaurants in which towns should we go to next, weighing cost, drivability, routing efficiency, demographics, and store popularity. Should we prefer big cities or small towns?

• Empower Commercial leaders by replacing static reports with dynamic tools, such as always-up-to-date partner dossiers, so the team negotiates with evidence rather than opinions.

On the operational side:

• Be the analytical partner to Field Operations. Their days are full of concrete problems: robots idle in one hub while another misses deliveries, broken bots waiting for pickup, spare parts running out, fixes taking too long, spillages, etc. Your job is not to answer each one, but to help them frame the problem, put a number on it, set a target worth hitting, and check whether the fix worked. Most of these come back monthly with a different site attached, so building the logic once beats answering it eleven times.

• Zoom out. Operations is, well, operational. Someone needs to step back and say what the biggest opportunities actually are - whether that is partner satisfaction, customer experience, cost per delivery, or revenue per robot - rather than waiting to be asked. That someone is you.

What’s in it for you?

• A team worth joining. Small, senior, and self-aware. We ask for feedback, we take it, and we take pride in getting a lot done with limited resources, tackling problems nobody has solved before.

• Real access, real influence. You will work with people at every level, from specialists to senior leadership across Commercial, Operations, Engineering and Product, as a partner rather than a service desk. Your analysis drives our decisions: whether we scale a market, where we go next, what we fix first. When the answer goes into a partner negotiation or a board discussion, you are the one who helps make sure it holds.

• You are not starting from scratch. The platform is established: dbt, Databricks, semantic layer, Claude wired into our data stack, and pipelines that work. You inherit a foundation and build on top of it rather than rebuilding it.

• The questions are unusually good. Because the product is physical and the business is multi-sided, an average question here needs several worlds joined together. Working out whether a town is worth entering means combining robot telemetry, partner order data, walking distances and terrain, human courier ETAs, and demographics. Working out why ETAs are drifting means separating autonomous driving behaviour from operational realities and weather conditions. There is no textbook and no playbook. You will connect datasets that were never designed to meet.

• Room to grow. We are a fast-growing company where your contribution is visible, and there is plenty of room to grow - by deepening your expertise, taking on bigger challenges, or moving into new areas.

• A flexible hybrid model. The team works mostly from home and gathers at the office 1-2 times a week.

What we hope you’ll bring to the table:

• Solid experience in an analytical role, with a track record of business, growth or product analysis rather than reporting: Data, Business, BI, Growth or Product Analyst, Data Scientist, Solutions Engineer or similar.

• Strong SQL skills, working with complex datasets.

• Experience driving complex topics end-to-end, from a rough idea to a proposed plan, to executing the solution, and presenting it so that it is understandable and useful to everyone.

• Pragmatic can-do mindset. You focus on impact rather than the beauty of the method, and decide pragmatically when a spreadsheet is enough and when the answer is a dashboard, a pipeline or an ML model.

• The ability to get your point across to anyone, whether it is the Autonomous Driving team, the Commercial team or senior management.

• Judgement about what matters. You can tell urgent apart from important apart from impactful, and you know the most urgent thing is usually neither of the other two.

• Fluent English, written and spoken.

• Beneficial: dbt, Databricks, Python or R, and experience with semantic or metric layers.

Want to learn more about our robots and our people? Get in touch and let’s have a chat!