> Markdown version of [/jobs/ext/2000885-senior-data-scientist](https://www.wearedevelopers.com/jobs/ext/2000885-senior-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** adjoe GmbH - **Location:** Hamburg, Germany - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Python (Programming Language), NumPy, Tensorflow, Software Deployment, Sql Optimization, Pytorch, Apache Spark, Deep Learning, Pandas, Scikit Learn, Xgboost - **Published:** August 9, 2026 - **Apply:** https://www.adzuna.de/details/5833705947 ## About the Role * Proven track record in production ML. You have 5+ years in Data Science with a history of deployed models that moved real business metrics, not just research that stayed in notebooks. At adjoe, you'll be building models that predict LTV, conversion, and user behavior across 770 million users, directly impacting advertiser ROAS and publisher revenue. * Deep learning is your primary tool. You have strong hands-on experience with PyTorch or TensorFlow and have deployed deep learning models in production environments handling 1M+ daily predictions. You know the difference between a model that works in evaluation and one that holds up under real traffic. * Full ML lifecycle ownership. You own the problem end-to-end from extracting insights out of terabytes of behavioral data using Trino, Spark, or AWS Athena, through model development and A/B validation, to production deployment. Experience with Airflow or model hosting is a strong plus. * Fluent in both Python and data at scale. You work comfortably with the core DS stack (pandas/polars, numpy, scikit-learn, LightGBM, CatBoost, XGBoost) and have advanced SQL skills for drilling into large distributed datasets. * Bridges ML and product. You can translate product requirements into ML logic and explain model behavior and impact to non-technical stakeholders without losing the technical depth underneath. AdTech experience is a strong plus. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [The Biggest German Tech Companies](https://www.wearedevelopers.com/magazine/424-the-biggest-german-tech-companies) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)