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About the Role

You will be the scientific conscience of our data products, turning billions of location signals into production-ready models and proving their effectiveness. You will own the data science of an entire domain, from business questions to live model impact, setting methodological standards, reviewing others' work, and collaborating with Product on strategic questions.

Responsibilities

Build and ship models that answer business questions.
Design, validate, and deploy models for causal campaign measurement, audience classification, visitation forecasting, place and audience embeddings, and recommendation.
Own methods that estimate advertising impact, including counterfactual and synthetic-control models, Bayesian structural time series, difference-in-differences, and uplift models.
Quantify uncertainty and state assumptions for causal impact measurements.
Build time series models for footfall, reach, and audience size, including hierarchical Bayesian models and neural forecasters.
Learn and utilize vector embeddings of places, devices, and audiences for similarity search, collaborative filtering, and lookalike modeling.
Fine-tune and evaluate LLMs for use cases like classifying places and audiences, extracting text structure, and agentic data workflows.
Use coding agents to write analysis code, run experiment grids, draft reports, and design guardrails for reproducible and correct agent-run experiments.
Write the team's methodological playbook, including experiment design, power analysis, validation protocols, and review checklists.
Review other data scientists' work rigorously and fairly, and mentor senior and mid-level colleagues.
Think 12 to 24 months ahead to shape the roadmap, bringing state-of-the-art research into products, and identifying high-value R&D opportunities.
Write clear technical papers and internal reports, and explain complex algorithms to stakeholders in terms of decisions, risks, and financial impact.
Represent Adsquare in peer-reviewed publications and at conferences.

Requirements

7+ years of hands-on data science experience, or 4+ years post-PhD, with several production models and measurable business impact.
Deep understanding of the mathematics behind methods used, including probability theory, inferential statistics, linear algebra, optimization, and experimental design.
Ability to derive estimators, identify violated assumptions, and explain model failures.
Experience working by the scientific method, including designing pre-registered experiments, performing power analyses, building leakage-free validation schemes, and adapting to data-driven conclusions.
Proven experience estimating causal impact from observational and experimental data.
Production experience with classifiers, vector embeddings, similarity search, and recommender methods.
Daily use of coding agents (e.g., Claude Code, Cortex) for data science experiments, with the ability to check and review their output.
Experience with TB-scale, complex data in SQL and Python (e.g., Athena/Trino, Spark, BigQuery, Snowflake), optimizing for speed and correctness.
Professional-level Python coding skills (typed, tested, reviewed).
Experience deploying and monitoring models in a cloud environment, preferably AWS.
Excellent scientific writing skills and ability to explain complex algorithms to business stakeholders.
Ability to link model metrics to revenue, cost, and customer outcomes.
Understanding when a simpler model is the better business choice.
Adherence to core values: Drive, Resilience, No BS, Humble, and Moral Compass.
Care for privacy when working with location data.

Skills

Probability theory
Inferential statistics
Linear algebra
Optimization
Experimental design
Causal inference
Classifiers
Vector embeddings
Similarity search
Collaborative filtering
Recommender systems
Coding agents (Claude Code, Cortex)
SQL
Python
Athena/Trino
Spark
BigQuery
Snowflake
AWS
Scientific writing
Geo-spatial data science
H3 grids
S2 grids
Spatial joins
Coordinate reference systems
Mobility data
Bayesian statistics
Hierarchical models
PyMC
Stan
NumPyro
Time series forecasting
State-space models
Structural models
Hierarchical forecasting
Neural forecasters
Neural networks
PyTorch
TensorFlow
Keras
Representation learning
LLM solutions
Model fine-tuning (LoRA)
Programmatic advertising
Ad measurement
Location data

Location

Remote (up to 3 months a year)
Hybrid

Work Type

Hybrid
Remote

Experience Level

7+ years of hands-on data science
4+ years after PhD

Education Level

Master's degree
PhD in mathematics, physics, statistics, or another natural science or quantitative field

Salary/Compensations

92,000 - 107,000 (base + bonus)

Benefits

Competitive package
Up to 3 months a year working remotely from anywhere
Personal learning budget of 1,200 EUR a year
30 vacation days a year
Urban Sports Club membership
Company pension scheme
Relocation / home office budget
Regular team and company events
Latest hardware
All tools needed to thrive

About the Company

Adsquare is the global audience & location intelligence company making marketing smarter.
We are a truly international company with eight offices worldwide, united in empowering companies to accelerate business growth through data-driven marketing.
We are pioneers in advertising and data, born mobile-first, and have led data-driven advertising in display and DOOH.
We believe in innovation, the power of technology, and constant improvement.
We are visionaries who believe in a better way for marketers and are committed to making it a reality.
We believe in what we do, rise to meet high standards, and fix problems humbly and without delay.
We are unassuming and modest, despite having best-of-breed technology.
We are passionately candid, explaining our approach transparently.
We are consistent and true to our word, focusing on being our clients' most reliable partner.
Clients enjoy spending time with us because we are good people.