scikit-learn Experts in Munich
matched in minutes by AIHire experts who deliver classification, regression and clustering solutions with scikit-learn, Python and the wider data science ecosystem. FRATCH matches you quickly with vetted, available freelancers whose skills fit your project.
Meet FRATCH Experts in Munich, who have recently used scikit-learn
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Giuseppe A.
Last position:
Embedded Software Development at Inheco
- AI Integration (LLM & RAG): Designed and built an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data to automatically analyze technical documents and error logs.
- Design & Implementation: Designed a robust RS-232/UART communication interface for an SBC-based embedded device used to control medical shaker systems.
- Architecture & Protocol Design: Built a highly maintainable software structure (OOP, SOLID) and defined resilient, hardware-level communication protocols, including multithreading and advanced error handling.
- Quality Assurance & DevOps: Automated testing with xUnit, ran integration tests directly on the hardware target, and maintained technical documentation to strict medical technology standards via Azure DevOps.
Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps
Emanuel F.
Last position:
Interim Architect & Data Taskforce at Freelancer / Project Assignments
- Data Engineering: Design and implementation of scalable data pipelines
- Legacy migrations to Microsoft Fabric (Lakehouse, Dataflows Gen2, Pipelines)
- BO Universe migrations to MS Fabric / Semantic Models / Power BI
- Taskforce for data-driven transformation projects involving Azure Fabric / Oracle / MSSQL
Thomas H.
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
- Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
- Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
- Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
- Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
- Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
- Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integration of RESTHeart to create a REST API for MongoDB.
- Build an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).
Sukumar R.
Last position:
AI Consultant
- Architect end-to-end AI/ML solutions, with a strong focus on advanced LLM applications, autonomous agentic AI workflows using MCP toolset, vLM embedding computations, and robust agentic RAG pipelines to drive real-world business impact for clients across diverse sectors.
- Designed intelligent agentic AI workflows to automate knowledge retrieval and reasoning for B2B clients, accelerating business impact.
Axel K.
Last position:
Data Engineer & Business Analyst at Metafinanz
- Migration of existing data jobs from Cognos Data Manager to Tibco/IBI Datamigrator
- Migration data jobs parametrisation for dynamic runs
- Optimisation and cutting-back
- Regression tests
- Knowledge transfer and documentation
Mario G.
Last position:
Software and Data Engineer at Plexify GmbH
- Architecture, design and development of an MVP application for a provider of specialized travel experiences
- Technologies: Python, FastAPI, Firestore, Firebase, Docker
Narges D.
Last position:
Research Assistant at Hochschule München
Introduced an integrated approach for structural damage detection across concrete, steel, and glass using advanced technologies such as LiDAR and thermal imaging. Highlighted cross-material interactions to enhance diagnostics and enable predictive maintenance.
Developed an NLP-based medical note simplifier that transforms complex clinical instructions into plain, child-level English. Applied prompt engineering with Flan-T5 transformer models to extract patient-relevant actions and rephrase them into clear to-do items. Built dual Flask and Tornado backends with a printable web interface.
Tobias N.
Last position:
Enterprise & Solutions Architect
- Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
- Migration of all applications; avoiding high contractual penalties for the client.
- Onboarding and coordination o...
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Caner K.
Last position:
Synthetic Medical Dataset (MedGym) at MedTank
- Generated synthetic datasets for CXR, mammography, and distal radius fracture detection using GANs and diffusion, creating >50k synthetic images for benchmarking.
- Ensured GDPR-compliant workflows and reproducibility, enabling dataset adoption for internal validation and academic collaboration.
- Project highlighted in MedTank’s internal R&D showcase as a flagship synthetic data initiative.
Luis Alberto P.
Last position:
Cloud Engineer at Personal Projects
Developed a Streamlit ML application utilizing a RandomForest model (Scikit-learn) for predicting smoking behavior, employing Pandas, NumPy, and Matplotlib for data analysis and visualization; deployed on AWS using Terraform for EC2, IAM roles, and S3 buckets, with Pickle for model storage.
Mastered AWS services including S3, EC2, CloudFormation, IAM, and Auto Scaling, focusing on advanced features like versioning, CORS, ETags, and checksums through AWS-Examples-Freecodecamp.
Developed and optimized CI/CD pipelines with GitHub Actions to deploy static websites on GitHub Pages, enhancing automated validation, deployment, and maintenance processes.
Created and deployed a classic Snake game using Flask, containerized with Docker and deployed on Render.
Clarissa H.
Last position:
AI Trainer at Komdis GmbH
- Led comprehensive AI workshops for professionals, focusing on AI-driven process automation.
- Tech Stack: n8n, Make, LLMs (OpenAI, Anthropic), Prompt Engineering, Process Mapping Tools.
Krithika C.
Last position:
Professional Reorientation at Von Rundstedt
- Engaged in a structured career development program while strengthening German language proficiency (B1 level) and evaluating opportunities in ADAS/AD systems and requirements engineering.
Discover over 15,000 top freelancers
Statistics of experts using scikit-learn
Aggregated from the professional profiles of matched freelancers.
Experience
14 years (Germany: 11 years)
Position duration
1.8 years (Germany: 1.9 years)
Positions per freelancer
11 (Germany: 8)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
89% (Germany: 83%)
Doctorate
14% (Germany: 21%)
Certifications per freelancer
2
Most common languages
German, English, Spanish
Speak two or more languages
100% (Germany: 98%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using scikit-learn
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
scikit-learn experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (80%)
- Automotive (50%)
- Banking and Finance (47%)
- Manufacturing (47%)
- Education (43%)
- Professional Services (40%)
- Healthcare (37%)
- Government and Administration (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Machine learning foundation
scikit-learn is an open-source Python library for supervised and unsupervised machine learning. It provides consistent APIs for preparing data, training models, evaluating results and building repeatable workflows. Companies use it for practical prediction and pattern-recognition systems rather than deep neural network research.
What it builds
Common deliverables include customer churn models, demand forecasts, fraud detection, recommendation logic and document classification. Professionals also create reusable pipelines that combine data cleaning, feature engineering, model training and validation. The library supports both exploratory analysis and production-oriented batch or API workflows.
Ecosystem and tooling
scikit-learn works closely with NumPy, pandas, SciPy and Matplotlib, while tools such as Jupyter support experimentation. Strong specialists understand cross-validation, feature selection, model persistence and metrics that fit the business objective. They may also connect models to MLflow, cloud services, databases or web APIs.
When companies need specialists
- A proof of concept must become a tested model
- Training data needs cleaning, transformation or validation
- Model results are difficult to explain or reproduce
- Existing pipelines need performance and monitoring improvements
- A team needs practical machine learning knowledge without a long hiring process
Freelance expertise is useful when a project has a defined data problem but limited internal capacity. Specialists can assess feasibility, choose suitable algorithms and leave behind documented workflows that the team can maintain.
Quality signals
Good professionals compare a meaningful baseline with more complex models instead of chasing novelty. They prevent leakage, use suitable validation strategies and explain trade-offs in terms of precision, recall, cost and operational risk. They also make preprocessing part of the pipeline so training and inference behave consistently.
Working in Munich
Companies in Munich use scikit-learn across manufacturing, mobility, financial services, retail and research-oriented teams. Local collaboration can help with workshops, German-language communication and access to internal data, while remote specialists can contribute through secure repositories, clear documentation and scheduled reviews. The right setup depends on data access, compliance needs and stakeholder proximity.
Frequently asked questions
Not sure where to start with scikit-learn? These answers cover the essentials.
scikit-learn is used to train and evaluate classical machine learning models in Python. Typical applications include classification, regression, clustering, anomaly detection, dimensionality reduction and reusable preprocessing pipelines.
scikit-learn is usually the practical choice for structured data and established algorithms such as linear models, tree ensembles and support vector machines. TensorFlow and PyTorch are more suitable when a project requires deep neural networks, custom training loops or advanced handling of unstructured data.
A strong scikit-learn specialist usually works comfortably with Python, pandas, NumPy and SQL. Experience with experiment tracking, cloud infrastructure, APIs, data visualisation and model monitoring is also valuable when a model must operate beyond a notebook.
The required depth depends on the problem, data quality and operational risk. A small exploratory model may need focused scikit-learn expertise, while a customer-facing system calls for a professional who can handle validation, reproducibility, deployment and ongoing monitoring.
scikit-learn work is often suitable for remote collaboration when data access, repositories and environments are well defined. On-site sessions in Munich can still be useful for discovery workshops, stakeholder alignment and projects involving sensitive internal data.
Ask how the professional would establish a baseline, split the data and select metrics before comparing models. A capable scikit-learn specialist will discuss leakage prevention, error analysis, explainability and how the pipeline will be tested after deployment.
scikit-learn is a strong choice when a project benefits from proven algorithms, clear interfaces and fast iteration on structured data. Its conventions reduce unnecessary custom code, while custom components may be justified for specialised algorithms, unusual hardware or advanced deep learning workloads.
A scikit-learn engagement should produce a reproducible training pipeline, documented data assumptions, evaluation results and a clear model handover. Depending on scope, the professional may also provide an inference service, saved model artefacts, monitoring guidance and tests for future data changes.
The average hourly rate of freelancers in Munich, Germany who have used scikit-learn in their recent projects is 90 €, which corresponds to a daily rate of about 717 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used scikit-learn in their recent projects, 100% hold at least a Bachelor's degree, 89% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Munich, Germany who have used scikit-learn in their recent projects have 14 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Munich, Germany who have used scikit-learn in their recent projects are German (100%), English (100%), and Spanish (27%).
The most common industries among freelancers in Munich, Germany who have used scikit-learn in their recent projects are Information Technology (80%), Automotive (50%), and Banking and Finance (47%).
The most common business areas among freelancers in Munich, Germany who have used scikit-learn in their recent projects are Information Technology (93%), Product Development (83%), and Research and Development (70%).
Main locations of FRATCH Experts, who have recently used scikit-learn
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
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Nuremberg Request a free demo
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FRATCH CEO
