Intro
Adsquare does Location Intelligence for programmatic advertisers. We solve hard problems with good people, good technology and a lot of data.
Our core values guide how we work, and we hire for them:
Drive: we turn ambition into action that produces valuable outcomes.
Resilience: we adapt, persevere, and grow stronger through challenges.
No BS: we are honest, transparent, and clear.
Humble: we choose modesty over vanity and let results speak.
Moral Compass: we act with fairness, integrity and respect.
About the team
Data and evidence drive every decision we make. A claim that has not been tested on data it has never seen is a hypothesis, not a result. In practice, that means:
Business need first. Every algorithm starts with agreed-upon business-relevant success metrics before work begins.
Pre-registration. Hypotheses, primary metrics, analysis plan, and stopping rules are written down before the data is looked at.
Statistical power. Sample sizes and minimum detectable effects are calculated up front. We do not run experiments that cannot answer their question. And we aim to build ML models with appropriate sensitivity, specificity, and precision.
Independent tests, honest uncertainty. Every claim is tested on held-out data never used for model selection, and reported with intervals and its assumptions stated.
Reproducibility. Code, data versions, and seeds are recorded, so anyone, human or agent, can re-run the result.
Negative results count. A well-run experiment that kills an idea is a success.
You will own this standard, teach it, and raise it.
Your Mission
You will be the scientific conscience of our data products: the person who turns billions of location signals into models that work in production and can prove that they work.
You own the data science of a whole domain, from the business question to the live model and its measured impact. You set the methodological standard for the team, review the work of other data scientists, and decide with Product which questions are worth answering and formulate answers always with the necessary scientific evidence.
You will work in the Data Science team, which is part of the Data Department, alongside other data scientists and ML engineers. Using geospatial data and audience attribute data, your tasks include developing and evaluating algorithms for causal impact measurement. We ask questions like: how much did a campaign change real-world visits, which audiences does a place attract, what will footfall look like next month, and how can AI help to answer these questions faster and better.
Your Profile
Experience: 7+ years of hands-on data science, or 4+ years after a PhD, with several models you designed running in production and a measured business effect you can name.
Mathematical depth: you know the maths behind the methods you use: probability theory, inferential statistics, linear algebra, optimisation and experimental design. You can derive an estimator, spot a violated assumption, and explain why a model fails.
Scientific rigour: you work by the scientific method. You have designed pre-registered experiments, done power analyses, built leakage-free validation schemes, and changed your mind when the data said so.
Causal inference: proven experience estimating causal impact from observational and experimental data, for example synthetic control, Bayesian structural time series, Geo-Lift, difference-in-differences, or uplift modelling.
Core ML toolkit: production experience with classifiers, vector embeddings and similarity search, and collaborative filtering or other recommender methods.
Agentic coding (must-have): you work daily with coding agents such as Claude Code or Cortex, use them to run data science experiments, and know how to check and review their output.
Large, messy data: you have worked with TB-scale, complex data in SQL and Python (for example, Athena/Trino, Spark, BigQuery, or Snowflake) and know how to make it fast and correct.
Production mindset: Python…