scikit-learn Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used scikit-learn
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Giuseppe Abrignani
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
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Thomas Hoefkens
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 Chand
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.
Tobias Nawa
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...
Axel Kraus
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
Christian Schulz
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 Vadali
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 Dirndorfer
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
Clarissa Heinemann
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.
Stephan Baier
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Maziyar Khorrami
Last position:
Data Engineer at MSD Germany
- Lead Architect to design and implement the data lake and ETL Pipeline using AWS Stack
- Performance Optimization of Data Ingestion of ETL Pipeline
- Development of Data Validation using Great Expectations
- Leading of the data migration for two sources exchanges
- Data Modeling in AWS Redshift
MLOps
- Model inference implementation by mlflow and AWS SageMaker
- Feature Engineering for the running ML Models ( Recommender Engineer, Clustering )
- Implementatino of Model Registry and artifactory using mlflow
- Historization an Profiling of the Input Data Using AWS Glue Crawler and AWS Data Catalog
- Feature importance using mlflow
Tech. Stack: Python 3, AWS Glue, AWS Step Fucntion, AWS Lambda, AWS EventBridge, AWS IAM Role, AWS SageMaker, AWS EC2, AWS Glue Crawler, AWS CloudWatch, MLFlow, ETL, Data lake, GitHub Action, Terraform, Jenkins, Ansible playbooks (Infrastructure as Code), CI/CD, GitLab, SQL, PySparkSCRUM, Agile, Jira, BigData, VSCode, DBeaver, MSSQL, MySQL, grafana, Docker, Linux, Bash, MapReduce, Data Modeling (ORM), Pandas, YAML, SQL-Alchemy
Narges Dastanpour Hosseinabadi
Last position:
Research Assistant at Munich University of Applied Sciences
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.
Oussama El Allam
Last position:
Head of R&D at eXagotec GmbH
- Spearheading multidisciplinary engineering teams in the development of next-generation medical devices
- Orchestrating research initiatives and technology roadmaps to deliver innovative medical solutions
- Overseeing R&D budget and managing project portfolios from concept through to commercialisation
- Establishing strategic collaborations with clinical partners for technology validation
Discover over 15,000 top freelancers
Statistics of experts using scikit-learn
Aggregated from the professional profiles of matched freelancers.
Experience
13 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, Education
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
96% (Germany: 99%)
Master's degree or higher
89% (Germany: 83%)
Doctorate
15% (Germany: 20%)
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 30 Aug 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Model work
scikit-learn is a Python library for building classic machine learning models. It is used for classification, regression, clustering, dimensionality reduction, and model selection. Teams bring it in when they need reliable experiments and production-ready model logic, not heavy framework overhead.
Typical use cases
- Predict churn, demand, or risk from tabular data
- Build text classifiers and ranking baselines
- Clean data, scale features, and reduce noise
- Compare algorithms with cross-validation and metrics
- Package reusable training and inference code
Core ecosystem
Strong specialists work comfortably with NumPy, pandas, SciPy, and matplotlib around scikit-learn. They know pipelines, transformers, encoders, model persistence, and parameter search. In Munich, that skill set is common in manufacturing, insurance, mobility, and analytics-heavy product teams.
When to bring help
Companies ask for freelance expertise when a model behaves inconsistently, a proof of concept needs hardening, or an internal team lacks time to tune and document the workflow. The same applies when sklearn code must fit into a Python service, data science notebook, or batch scoring job.
What strong specialists do
Good professionals do more than fit an estimator. They choose sensible baselines, avoid leakage, explain trade-offs, and write pipelines that are easy to test and maintain. They also understand when scikit-learn is the right tool and when a different approach is better.
Collaboration details
For Munich-based teams, scikit-learn work is often hybrid. On-site sessions help with stakeholder workshops and model reviews, while implementation and iteration can stay remote. The best experts communicate clearly, document assumptions, and hand over code that other specialists can extend.
Frequently asked questions
Not sure where to start with scikit-learn? These answers cover the essentials.
scikit-learn is used for classic machine learning in Python, especially on structured data. It helps teams build classifiers, regression models, clustering workflows, and preprocessing pipelines without adding a lot of framework complexity.
scikit-learn is usually the better fit when you need fast, clear models for tabular data, feature engineering, and evaluation. TensorFlow and PyTorch are more common for deep learning, image work, and neural network-heavy systems. Many teams use sklearn first, then move to a deeper stack only if the problem needs it.
A strong scikit-learn specialist should also know Python, pandas, NumPy, and basic statistics. Familiarity with model validation, feature encoding, pipelines, and metric choice matters a lot. For production work, experience with testing and model serialization is helpful too.
scikit-learn projects benefit from freelance help as soon as the model work starts affecting decisions, not only at the end. If the first baseline already needs tuning, better validation, or cleaner code, a specialist can save time and prevent rework. Small internal teams often bring in help earlier than they expect.
Yes, much of the scikit-learn work can be done remotely because the core tasks are code, data, and review. Munich teams often combine remote implementation with a few in-person working sessions for requirements, feedback, or model sign-off. That setup works well when the specialist communicates clearly and documents decisions.
Look for a scikit-learn professional who explains why a model was chosen, how leakage was avoided, and how results were validated. Good code should use pipelines, keep preprocessing consistent, and make retraining straightforward. Clear notebooks, clean modules, and practical trade-off discussions are strong signs.
scikit-learn can be enough for many production use cases, especially for scoring tabular data and maintaining interpretable workflows. It becomes less suitable when you need large-scale deep learning or highly specialized architectures. A good specialist will tell you where sklearn fits cleanly and where another stack is a better choice.
In Munich, scikit-learn projects often center on forecasting, quality checks, customer analytics, and operational decision support. Companies usually want a specialist who can work with local stakeholders, align with existing Python tooling, and hand over code that fits into broader data workflows. Language needs are often flexible, but clear English communication is expected.
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 723 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used scikit-learn in their recent projects, 96% hold at least a Bachelor's degree, 89% 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 13 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 (76%), Automotive (48%), and Education (45%).
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 (72%).
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.
Berlin 
Hamburg 
Cologne 
Frankfurt 
Stuttgart 
Nuremberg Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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Philipp Thomaschewski
FRATCH CEO
