TyrAds is a leading tech-driven loyalty and rewards platform that partners with businesses to create meaningful and rewarding experiences for their customers.
With millions of users across our platforms, we specialize in innovative AdTech solutions powered by big data, machine learning, and deep learning technologies.We build seamless user experiences across web, Android, and iOS, while leveraging ML/AI to power personalization and advertising effectiveness.
Our product development follows Agile methodologies with weekly releases and collaboration tools such as GitHub, Jira, and Slack.
TyrAds is a leading tech-driven loyalty and rewards platform that partners with businesses to create meaningful and rewarding experiences for their customers.
With millions of users across our platforms, we specialize in innovative AdTech solutions powered by big data, machine learning, and deep learning technologies.We build seamless user experiences across web, Android, and iOS, while leveraging ML/AI to power personalization and advertising effectiveness.
Our product development follows Agile methodologies with weekly releases and collaboration tools such as GitHub, Jira, and Slack.
We are hiring a Senior Data Scientist to work alongside our Product team and turn our reward economy into models the business can act on.
Our machine learning engineering team owns real-time serving, latency and model monitoring.
You define the model,



you design the experiment that tests it, and you produce the measured result.
They serve it.
The problems are commercial and they are hard.
5+ years as a data scientist or quantitative analyst in a product, marketplace or performance marketing business
Applied statistics: experiment design, power analysis, confidence intervals, and the discipline to report a null result as clearly as a positive one
Causal inference beyond A/B testing: difference in differences, matching, holdouts, incrementality
Advanced SQL on large event tables (CTEs, window functions)
Python for modelling and analysis (pandas, numpy, scikit-learn)
Comfortable with the defects of real event data: duplicates, late arrivals, rejected installs, inconsistent partner data
Can explain a model to a commercial stakeholder without simplifying it into something that is no longer true
MMP data (AppsFlyer, Adjust) and the limits of multi-touch attribution
Pricing, incentive or elasticity modelling in a commercial setting
Databricks or Spark
Model payout against funnel progression, event design against completion, and user quality against advertiser return
Difference in differences, matching, holdouts, incrementality testing
Say clearly when the available data cannot answer the question
Making It Usable
Not Data Engineering: Pipelines are built and maintained by Data Engineering.

📌 Data Scientist Senior - Machine Learning (Barcelona)
🏢 Tyrads
📍 Barcelona