Senior Data Scientist, LTV & Growth Modeling

Scrambly; Italia; In loco; EUR 70.000 - 100.000; Tempo pieno

Descrizione del lavoro

Scrambly is hiring a Senior Data Scientist to own end-to-end modeling for how users are valued, acquired, and rewarded. You will forecast value from early signals and partner with engineers to deploy predictions that steer monetisation and growth.

You will lead the modelling lifecycle, from framing problems to live evaluation, collaborating with a small, fast-moving data team and reporting to the Head of Data. This early hire shapes the data function.

Competenze

• 4+ years of production ML experience in a data-driven environment.
• Experience with LTV/ROAS forecasting and long-horizon cohorts.
• Background in adtech or mobile performance marketing and attribution data.
• Strong Python and SQL, with cloud data warehouse experience (BigQuery preferred).
• Rigorous evaluation mindset and ability to ship models.
• Commercial sense with clear communication for non-technical stakeholders.
• Self-directed, small team work style.
• English at B2+, written and spoken.

Mansioni

• Value prediction: forecast user and cohort value from early signals.
• Bidding and pricing: determine spend to acquire users within margins.
• Segmentation and targeting: build scoring and segmentation for value.
• Fraud and abuse: improve fraud models while avoiding false positives.
• Evaluation: define success metrics and monitor model performance post-launch.
• Collaboration: work with Head of Data, engineers, and commercial teams to operationalize model output.

Conoscenze

Python

SQL

LTV modelling

Adtech experience

Communication

English (B2+)

Strumenti

BigQuery

dbt

BQML

Descrizione del lavoro

Scrambly is hiring a Senior Data Scientist to own end-to-end modeling for how users are valued, acquired, and rewarded. You will forecast value from early signals and partner with engineers to deploy predictions that steer monetisation and growth.

You will lead the modelling lifecycle, from framing problems to live evaluation, collaborating with a small, fast-moving data team and reporting to the Head of Data. This early hire shapes the data function.