Time since we first saw it
Day 1
Early. Similar jobs usually come down between day 7 and day 56.
From 10,380 similar postings that came down. Coming down is not always a hire.
Frame business problems where AI and machine learning can deliver measurable value
Design and build machine learning pipelines from prototyping to production
Develop, fine-tune and optimize AI models, including LLMs and generative models
Set up and maintain AI/ML infrastructure with MLOps best practices
Create APIs and integrations so stakeholders can use AI insights in real time
Partner with product and business teams to understand challenges, identify opportunities, and frame problems where AI and machine learning can deliver measurable value
Design and build machine learning pipelines, from experimentation and prototyping to production deployment
Develop, fine-tune, and optimize AI models (including LLMs and generative AI models)
Set up and maintain AI/ML development and production infrastructure, leveraging MLOps best practices (CI/CD, monitoring, observability, model versioning)
Create APIs, integrations, and tools that enable stakeholders to operationalize AI insights in real-time environments
Strong experience designing, developing, and deploying AI/ML products or models in production
Strong programming skills in Python
Hands-on experience with LLMs, RAG systems, and vector databases
Proven experience building and fine-tuning models with TensorFlow, PyTorch, and Keras
German speaking (C1 at least)
Actively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 1
Early. Similar jobs usually come down between day 7 and day 56.
From 10,380 similar postings that came down. Coming down is not always a hire.
251 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Qualifications Where You'll Make an Impact Partner with product and business teams to understand challenges, identify opportunities, and frame problems where AI and machine learning can deliver measurable value Evaluate and select the most suitable AI approaches, including LLMs, RAG systems, classical ML, or multimodal AI Design and build machine learning pipelines, from experimentation and prototyping to production deployment Develop, fine-tune, and optimize AI models (including LLMs and generative AI models) while ensuring scalability, performance, and responsible use of AI Set up and maintain AI/ML development and production infrastructure, leveraging MLOps best practices (CI/CD, monitoring, observability, model versioning) Build robust data ingestion, transformation, and feature engineering pipelines to support high-quality AI applications Identify and curate high-value training datasets, leverage transfer learning, and adapt pre-trained models to business-specific contexts Create APIs, integrations, and tools that enable stakeholders to operationalize AI insights in real-time environments Help stakeholders understand AI outcomes, limitations, and trade-offs, ensuring transparency, interpretability, and alignment with business goals Continuously research and recommend emerging AI capabilities (LLMs, multimodal models, autonomous agents) that can improve products or processes What You'll Bring to the Team Strong experience designing, developing, and deploying AI/ML products or models in production Strong programming skills in Python Solid experience with data manipulation and ML tooling: NumPy, Pandas, scikit-learn, etc.
Proven experience building and fine-tuning models with TensorFlow, PyTorch, and Keras Hands-on experience with LLMs, RAG systems, and vector databases Familiarity with NLP, generative AI, prompt engineering, embeddings, and multimodal architectures Strong understanding of data science methodology, applied statistics, ML/DL, and AI evaluation frameworks Experience with MLOps and cloud-native development: Docker, Kubernetes, CI/CD, feature stores, and model monitoring Practical experience with cloud AI platforms Strong algorithmic and problem-solving skills, with the ability to deliver end-to-end solutions under time constraints German speaking (C1 at least) Consulting background
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New Data Scientist postings at the employers we follow are down 3% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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Berlin, DE
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