About us:
Tyr Ads 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 Ad Tech solutions powered by big data, machine learning, and deep learning technologies. Our systems process over 10,000 events per second, delivering real-time insights at scale. We build seamless user experiences across web, Android, and i OS, while leveraging ML/AI to power personalization and advertising effectiveness. We are a team of 90+ employees worldwide and growing, guided by values of transparency, ownership, learning from mistakes, and respect for diverse cultures. Our product development follows Agile methodologies with weekly releases and collaboration tools such as Git Hub, Jira, and Slack.
Job Description
About us:
Tyr Ads 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 Ad Tech solutions powered by big data, machine learning, and deep learning technologies. Our systems process over 10,000 events per second, delivering real-time insights at scale. We build seamless user experiences across web, Android, and i OS, while leveraging ML/AI to power personalization and advertising effectiveness. We are a team of 90+ employees worldwide and growing, guided by values of transparency, ownership, learning from mistakes, and respect for diverse cultures. Our product development follows Agile methodologies with weekly releases and collaboration tools such as Git Hub, 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.
This is a quantitative modelling role, not a production ML role. 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. A single campaign can carry up to 50 rewarded events. Lower the payout on one of them and unit margin improves, users stop progressing, and advertiser revenue falls. Today those trade-offs are set by experience. Your job is to replace experience with measurement.
Job Requirements
Modelling 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 Technical 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 Ways of working Can explain a model to a commercial stakeholder without simplifying it into something that is no longer true




Works well alongside a Head of Product and a COO who are both hands-on in the same problems Nice to Have Mobile advertising, user acquisition, offerwall or rewarded advertising
MMP data (Apps Flyer, Adjust) and the limits of multi-touch attribution
Pricing, incentive or elasticity modelling in a commercial setting
Databricks or Spark
Fraud and anomaly detection Job Responsibilities
Reward Economy Modelling Model payout against funnel progression, event design against completion, and user quality against advertiser return
Produce elasticity estimates that can be used to configure a campaign, not only to describe one
Size the opportunity in revenue or margin before the work starts Experimentation Design controlled experiments: randomisation unit, power analysis, primary and guardrail metrics
Set run time from the funnel's own event timing, and agree stopping rules before launch
Write a decision record for every experiment, including the ones that fail Causal Inference Run isolation reads before any causal claim, separating the effect of a funnel change from the effect of a change in publisher mix
Difference in differences, matching, holdouts, incrementality testing
Say clearly when the available data cannot answer the question Making It Usable Hand models that go live to the ML engineering team with an evaluation specification they can serve against
Write findings that Ad Ops and Sales can act on the same day, without a translation layer What this role is not: Not Production ML: API serving, latency, and drift are owned by ML Engineering.
Not Data Engineering: Pipelines are built and maintained by Data Engineering.
Not Dashboarding: Reports are a byproduct, not the core deliverable. #J-18808-Ljbffr

📌 Senior Data Scientist Eu (Barcelona)
🏢 Tyrads
📍 Barcelona