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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Science & Advanced Analytics - **Company:** adesso SE - **Location:** Germany - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Program Optimization, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Apache Hadoop, Monitoring of Systems, Python (Programming Language), Logical Volume Manager, Machine Learning, SAP ERP, NumPy, Recommender Systems, Tensorflow, Standard Sql, Azure Machine Learning, Management of Software Versions, Data Logging, Data Processing, Feature Engineering, Chatbots, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Model Validation, Generative AI, Pandas, Build Management, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Machine Learning Operations, Stream Processing, GPT, Data Pipelines, Docker, Unsupervised Learning, Databricks, Microservices - **Published:** August 6, 2026 - **Apply:** https://jobs.adesso-group.com/talentcommunity/apply/1363103155/?locale=en_GB ## About the Role * 5-8 years in Data Science, Machine Learning, or AI. * Proven end-to-end ML model development, deployment, and production support. * Hands-on predictive modeling using Python and modern ML frameworks. * Experience with cloud ML platforms (Azure ML, AWS SageMaker, or Google Vertex AI). * Experience developing GenAI solutions using LLMs, prompt engineering, and RAG frameworks. * Experience collaborating with Data Engineering and ML Engineering teams. * Agile experience delivering enterprise-scale AI applications., * B.Tech/B.S./M.S. in Computer Science, Statistics, Mathematics, or related field. Technical Skills * Programming: Python (mandatory), SQL * ML Libraries: Scikit-learn, TensorFlow, PyTorch * Data Processing: Pandas, NumPy, Spark * ML & AI: Supervised & unsupervised learning, model optimization * Recommender Systems: engine design, ranking algorithms, personalization * Generative AI: LLMs (GPT, Llama, etc.), prompt engineering, RAG (LangChain, LlamaIndex); multimodal AI (LVM) a plus * MLOps: CI/CD for ML, Docker, Kubernetes, model monitoring * Data Engineering: pipelines, ETL, data warehousing, * Strong communication, stakeholder management, organizational skills * Self-motivated, customer-focused, detail-oriented * Azure ecosystem experience (Azure ML, Databricks) * Exposure to real-time data processing * ML/AI/Cloud certifications * SAP ERP knowledge strongly preferred * Six Sigma Yellow/Green Belt a plus * ITIL certification a plus ## Description We are seeking a highly skilled Data Scientist with strong expertise in Machine Learning services, Recommender Systems (RS), and Generative AI (LLM & LVM). The role collaborates closely with Data Engineering, Data Science, and ML Engineering teams to design, develop, deploy, and scale intelligent data products and AI solutions., Data Science & Advanced Analytics * Develop and deploy end-to-end ML models from ideation to production. * Perform EDA, feature engineering, and model evaluation. * Build predictive and prescriptive models using statistical and ML techniques. Machine Learning Services (Primary Focus) * Design and implement scalable ML pipelines for training, testing, and deployment. * Work with ML platforms: Azure ML, AWS SageMaker, GCP Vertex AI. * Implement model lifecycle management: versioning, monitoring, retraining. * Optimize models for performance, scalability, and reliability. Recommender Systems (RS) * Design and build recommendation engines: collaborative, content-based, hybrid. * Work with large-scale datasets for ranking, personalization, user segmentation. * Evaluate models using precision@k, recall@k, NDCG. Generative AI (GenAI - LLM & LVM) * Build and deploy LLM-powered solutions: chatbots, copilots, document intelligence. * Implement RAG (Retrieval-Augmented Generation) architectures. * Work with models such as OpenAI, Azure OpenAI, Hugging Face. * Develop use cases for text generation, summarization, classification, and image/video understanding (LVM). * Optimize prompts and manage prompt engineering workflows. Collaboration with Data Engineering * Define data requirements; collaborate on data pipeline design. * Ensure data quality, governance, and availability. * Work with Spark, Databricks, Hadoop. ML Engineering & Deployment Support * Deploy models via APIs/microservices; containerize with Docker, Kubernetes. * Integrate models into production systems and CI/CD pipelines. Model Monitoring & Governance * Monitor model drift, performance degradation, and bias. * Implement logging, alerting, and explainability tools. * Ensure Responsible AI: fairness, transparency, interpretability. ## Related Videos - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Vectorize all the things! 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