scikit-learn Experts in Munich
matched in minutes by AIHire experts who build classification, regression and clustering workflows with scikit-learn, pandas and NumPy. Get precise access to vetted, available freelancers who can turn experimental models into maintainable data products.
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 Developer at Inheco
- AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
- Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
- Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
- Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according 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).
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.
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
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
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.
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.
Christian S.
Last position:
Data-Scientist/AI Engineer at The Marcom Engine GmbH & Co. KG
- Concept creation and implementing AI Agents in AWS Cloud
- Continuously alignment with stakeholders
- Collaborate with DevOps
- Technologies: Git, CI/CD (GitHub Actions), Python/ML, Streamlit, Deno/typescript, AWS SAM, AWS Bedrock, AWS Lambda, AWS Dynamo DB, AWS S3, AWS Event Bridge etc.
Raghu Ram V.
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Sebastian D.
Last position:
Data Scientist at CLADE GmbH
- Designed and implemented a robust Python-based data processing framework that supported the transition from R to Python and significantly improved data science productivity by providing maintainable, standardized modules for frequently used workflows, following coding best practices and DevOps principles
- Evaluated, trained, and deployed machine learning models on cloud platforms and edge devices, enabling fully automated mid-infrared (MIR) data evaluation pipelines that eliminated manual analysis steps and significantly shortened the time from measurement to prediction for customers and internal stakeholders
- Analyzed and interpreted multivariate MIR spectral data from the company’s proprietary analyzer using R and Python, supporting reliable identification and quantitation of chemical compounds in solution
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
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
1.7 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, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
93% (Germany: 83%)
Doctorate
15% (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 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge 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 19 Sep 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 (79%)
- Automotive (52%)
- Manufacturing (48%)
- Education (45%)
- Banking and Finance (45%)
- Professional Services (38%)
- Healthcare (34%)
- Insurance (28%)
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 practical machine learning. It provides consistent APIs for preparing data, training models, evaluating results and applying predictions. Companies use it to create repeatable workflows for structured business data without building core algorithms from scratch.
Models and use cases
The library supports supervised and unsupervised learning across many business problems:
- Classification for risk, churn and document categories
- Regression for demand, pricing and forecasting models
- Clustering for segmentation and pattern discovery
- Anomaly detection for unusual transactions or system behavior
Python ecosystem
Strong scikit-learn work depends on the wider Python data ecosystem. Professionals commonly combine it with pandas for tabular data, NumPy for numerical operations, SciPy for scientific computing and matplotlib or seaborn for analysis. Jupyter environments support exploration, while joblib can help persist trained models and parallelize suitable workloads.
Production workflows
A useful model needs more than a high evaluation score. Specialists create preprocessing pipelines, handle missing values, encode categorical data, select features and tune parameters without leaking information between training and validation data. They also package inference logic, document assumptions and connect predictions to APIs, batch jobs or existing applications.
When expertise helps
Companies often bring in freelance scikit-learn expertise when a proof of concept must become a dependable service, or when internal data teams need focused support:
- Define a sound validation and feature strategy
- Compare models against a meaningful baseline
- Improve reproducibility and experiment tracking
- Prepare a model for deployment and monitoring
In Munich, this can support product, manufacturing, finance, mobility and research teams. Remote collaboration works well when data access, review routines and technical ownership are clearly arranged.
Signs of quality
A strong professional explains why a model is appropriate, not only how to call its API. They understand leakage, imbalanced classes, calibration, explainability and the limits of offline evaluation. They write tested, readable Python and can communicate trade-offs to both data specialists and business stakeholders, including German-speaking teams when local collaboration requires it.
Frequently asked questions
Not sure where to start with scikit-learn? These answers cover the essentials.
scikit-learn is used to build machine learning workflows for structured data, including classification, regression, clustering and anomaly detection. It also provides preprocessing, model selection, evaluation and pipeline tools that help teams create repeatable experiments and production-ready prediction logic.
scikit-learn is usually the practical choice for tabular data and established statistical learning methods. TensorFlow and PyTorch are more suited to deep learning, custom neural network architectures and large-scale work with images, audio or language, although a project can use them alongside scikit-learn.
A strong scikit-learn specialist typically works comfortably with Python, pandas, NumPy and SQL. Experience with data validation, experiment tracking, model serving, Docker, cloud environments and monitoring is valuable when a model must operate beyond a notebook.
The right level depends on the task, data quality and production requirements rather than the library alone. A simple analysis may need focused support, while a regulated or business-critical system calls for a professional who can design validation, explain model behavior and manage deployment risks.
Yes, scikit-learn work is often well suited to remote collaboration because code, datasets, experiments and reviews can be managed digitally. Munich-based teams should clarify access controls, data residency, meeting language and the points where on-site workshops are genuinely useful.
Assess whether the professional separates training, validation and test data correctly and can explain the chosen metrics. Review pipeline design, reproducibility, error analysis, documentation and how predictions will be monitored after deployment, not just the headline model score.
Yes, scikit-learn models can support real-time services when preprocessing and inference are packaged consistently and latency requirements are understood. The surrounding API, serialization method, feature availability and monitoring design often matter as much as the estimator itself.
A common risk is finding someone who can train a model but has not handled leakage, changing data or deployment ownership. Ask for evidence of careful evaluation, maintainable pipelines and clear communication about uncertainty, especially when the model influences financial, operational or customer decisions.
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 721 € 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, 93% hold at least a Master's degree, and 15% 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 1.7 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 (28%).
The most common industries among freelancers in Munich, Germany who have used scikit-learn in their recent projects are Information Technology (79%), Automotive (52%), and Manufacturing (48%).
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 (69%).
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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FRATCH CEO
