Translate complex product requirements into actionable machine learning solutions.

Build robust models to predict user behavior, e.g. LTV and conversion prediction, optimizing retention, advertiser’s ROAS (return on advertising spend) and other dynamic values. 

Leverage state-of-the-art deep learning algorithms, ensuring each solution is fit-for-purpose and impact-driven.

Own the ML lifecycle from end-to-end: extracting insights from terabytes of user behavior data, developing models, validating them through A/B testing, and guiding them into production.

Partner closely with Backend, Infrastructure, and Analytics teams to ensure seamless model integration and system stability.

Present your findings and solutions to product and business stakeholders, clearly communicating the impact of your work on product KPIs.

You have 5+ years of professional experience in the Data Science field. You have a history of successfully deployed models that have made a measurable impact on business and product goals.

You have a strong knowledge of Python and main Data Science libraries: pytorch/tensorflow, pandas/polars, numpy, scikit-learn, lightGBM/catboost/xgboost.

You have experience developing deep learning models and have applied them already in a production environment with a large amount of traffic (>1 million predictions per day).

You have advanced SQL skills and experience drilling into large amounts of data using distributed query engines (e.g., AWS Athena, Trino, Spark).

You can explain complex mathematical foundations in simple, non-technical terms. You are comfortable in translating product language into ML logic and vice-versa.

You are open to relocating to Hamburg, Germany.

Big Plus: Experience in the AdTech industry.

Plus: Tech & Infrastructure knowledge: Airflow, hosting models, deploying models.