Senior Data Scientist EU

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.

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