Senior Data Scientist - LTV, Bidding & Fraud Modeling

Scrambly; Ireland; On-site; EUR 90,000 - 150,000; Full time

Job summary

Scrambly is seeking a Senior Data Scientist to own the models behind how we spend and earn money—forecasting user value, acquisition pay, and fraud controls. You will build end-to-end models and partner with engineers on data pipelines and deployment.

The role involves leading production-ready modelling, setting evaluation standards, and collaborating with a small team led by the Head of Data in a fast-growing, bootstrapped company.

Qualifications

• 4+ years as a data scientist with production models that drive decisions
• Experience with LTV/ROAS prediction from early behaviour and long horizons
• Adtech or mobile performance marketing background beneficial
• Strong Python and SQL with a cloud data warehouse (BigQuery preferred)
• Rigour in evaluation and out-of-sample validation
• Experience taking models into production with real business impact
• Commercial judgement and clear communication
• Proactive, self-directed work style
• English at B2+; written and spoken

Responsibilities

• Value prediction: forecast user and cohort value from early behaviour
• Bidding and pricing: model spend for user acquisition within margins
• Segmentation and targeting: scoring and segmenting users and inventories
• Fraud and abuse: improve fraud models and balance risk
• Evaluation: define metrics and maintain out-of-sample discipline
• Collaboration: work with Head of Data, data engineers, and commercial teams

Skills

Python

SQL

Communication

English B2+

Production models

Tools

BigQuery

dbt

Dataform

Job description

Scrambly is seeking a Senior Data Scientist to own the models behind how we spend and earn money—forecasting user value, acquisition pay, and fraud controls. You will build end-to-end models and partner with engineers on data pipelines and deployment.

The role involves leading production-ready modelling, setting evaluation standards, and collaborating with a small team led by the Head of Data in a fast-growing, bootstrapped company.