About us: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.Our systems process over 10,000 events per second, delivering real-time insights at scale.

We build seamless user experiences across web, Android, and iOS, 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 GitHub, Jira, and Slack.

About us

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.Our systems process over 10,000 events per second, delivering real-time insights at scale.

We build seamless user experiences across web, Android, and iOS, 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 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.

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 (AppsFlyer, 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 AdOps 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.

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