scikit-learn Experts in Frankfurt
matched in minutes by AIHire experts who deliver classification, regression and clustering workflows with scikit-learn, pandas and NumPy. Work with vetted, available freelancers matched precisely to your project and ready to collaborate from Frankfurt or remotely.
Meet FRATCH Experts in Frankfurt, who have recently used scikit-learn
Khaled T.
Last position:
Consultant / DevOps Engineer at Dr. Ing. h.c. F. Porsche Aktiengesellschaft
- Porsche ID is a unified digital identity platform providing secure authentication and seamless access across Porsche’s online services, mobile apps, and connected vehicle features
- Designed and implemented new authentication and authorization functionalities for both users and systems
- Ensured high availability, security, and performance to deliver a flawless digital experience for Porsche customers
- Technologies: Auth0, Angular, Tailwind, AWS, Terraform, Github
- Methodologies: Scrum and SAFe
Ashkan Z.
Last position:
Microsoft Azure Senior Data Engineer / Senior Data Scientist at Vattenfall Europe
- Advising on the use of analytics and BI tools and services in the Microsoft Azure stack (e.g. MS Fabric, Synapse Workspaces and dedicated SQL pools, SQL Database, PostgreSQL, Snowflake, Databricks, Data Factory, SSIS, Analysis Services, Function Apps, Power BI, ML)
- Independently designing analytics solutions with Python, SQL, etc.
- Designing and implementing ETLs and data pipelines
- Creating and maintaining APIs
- Independently applying CI/CD, testing, and version control
- Data modeling
- Model development and optimization
- Anomaly detection with AI
- Predictive analytics
Used technologies:
- Snowflake
- Fabric
- Azure Synapse Analytics
- Azure DataFactory
- Azure Data Lake
- Azure DevOps
- Databricks
- Spark
- CI/CD
- SQL Database
- Python
- Power Platform
Alona L.
Last position:
AI Architect
AI-powered platform for automated UX validation and designer support
- Designed and led technical implementation of an enterprise-wide AI solution for automated UX review that improved design quality and significantly reduced manual review processes in teams
- Developed an automated UX validation tool as a Figma plugin and web application that generates test cases based on internal guidelines and reliably checks current designs for consistency and standard compliance
- Implemented an interactive designer chat based on RAG that answers questions about the current design and the company's UX guidelines, and designed the deployment architecture using containerized services
- Python, Azure OpenAI, PostgreSQL, REST API, Docker, OpenShift, Helm, CI/CD, Figma MCP, LLM, RAG, Prompt Engineering, GenAI, XAI, AI Architecture, AI Strategy
Anton R.
Last position:
AI-Engineer at Publicly traded company, industrial safety technology
- Designed and implemented the agent-based AI architecture for a company-wide platform to securely deploy LLM-based agents
- Designed and implemented end-to-end RAG pipelines from multiple sources: document preprocessing, chunking strategies for different document types, embeddings, retrieval with re-ranking, and robust prompt orchestration
- Developed a modular context engineering framework with skill architecture, context isolation, and dynamic resource management; human-in-the-loop control for enterprise tool integrations
- Built the CI/CD pipeline, testing strategy, tracing on the software side as well as automated LLM and agent evaluations, red team testing and tracing, and handed over to a reproducible production environment (ISO27001 and SOC2 compliant)
Tan P.
Last position:
DevOps Engineer in the DevOps Team at Rise-World
- Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
- Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
- Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
- Use of Scrum and Kanban methods.
- Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
- Development of new plugins and add-ons needed on current infrastructure.
- Database support.
- Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
- Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
- Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
- Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
- Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
- Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
- Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
- Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
- Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
- Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
- Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
- Automated system provisioning and deployment using CloudFormation templates.
- Configuration of IAM roles, policies and permissions to ensure secure access control.
- Patch management, backup automation and disaster recovery setup on AWS infrastructure.
- Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
- Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
- Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
- Configuration of AWS CloudWatch to monitor application performance and system events.
- Planning and execution of migration of on-premises applications to AWS cloud platforms.
- Deployment of containerized applications using Docker and Kubernetes in AWS environments.
- Deployment of internal software packages between availability zones using AWS CodeDeploy.
- Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.
Yevgeniy Ö.
Last position:
Tester, Test & Data Analyst at NORD/LB
- Analyze system requirements and mapping concepts (ETL requirements) for data flows and transformation logic in the bank's DWH
- Analyze data in DB tables and views of the DWH using SQL (DB2)
- Independently define, create, and execute test cases in JIRA Xray (SIT)
- Write SQL queries in DB2 to verify data scenarios and mappings
- Create test plans for SAP FSDP and concurrent projects
- Conduct error and root cause analyses in coordination with business analysts, developers, test managers, and the infrastructure team
- Thoroughly document test results in JIRA Xray
- Coordinate between business analysis, development, DB infrastructure, business units, and external vendors
Kevin M.
Last position:
Freelance Lecturer in Coaching at DSI Education GmbH
- Practice-oriented coaching on core aspects of data science
- Teaching advanced concepts in Python as well as automation (with Make and n8n) and ETL processes with Apache Airflow
- Weekly preparation and delivery of practice-oriented programming courses using real-world examples
- Promoting practical programming skills among participants through interactive exercises and individual support
- Developing didactic materials and adapting content to participants' skill levels
- Close collaboration with the team for continuous improvement of course quality and learning outcomes
Jens D.
Last position:
Product Owner & Senior Data Scientist at Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Rashid I.
Last position:
Java Developer at IT company
- Data transformations
- IT company with more than 100 employees
- Software production
- Data augmentation and normalization, image transformation, format conversion, merging data from multiple sources
- Toolset: Java, Helm, Kubernetes, Kafka, OpenCV, IntelliJ IDEA, Gradle, Git, Docker, Containers, Scrum
Ahsan J.
Last position:
Data Analytics Developer at Level Next Productions
- Built Power BI dashboards and enabled data-driven strategies across digital platforms
Peka C.
Last position:
Data Warehouse Project for a Zoo at Alfatraining
- Created a complete entity-relationship model (ERM) for the future operational database
- Implemented the model using an RDBMS
- Designed and implemented a star schema for inventory management
Discover over 15,000 top freelancers
Statistics of experts using scikit-learn
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 11 years)
Position duration
1.6 years (Germany: 1.9 years)
Positions per freelancer
12 (Germany: 8)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
91% (Germany: 83%)
Doctorate
27% (Germany: 21%)
Certifications per freelancer
5 (Germany: 2)
Most common languages
German, English, French
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 Frankfurt 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 Frankfurt 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 (82%)
- Automotive (55%)
- Banking and Finance (55%)
- Education (45%)
- Healthcare (36%)
- Transportation (36%)
- Energy (27%)
- Insurance (27%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What scikit-learn does
scikit-learn is an open-source Python library for practical machine learning. It provides consistent APIs for preparing data, training models, tuning parameters and evaluating results. Teams use it to create forecasting, classification, recommendation and anomaly-detection solutions.
Core workflows
A typical project combines data preparation, feature engineering, model selection and validation. Professionals use pipelines to keep transformations aligned between training and production, while cross-validation helps expose overfitting before a model reaches users. Clear metrics and reproducible experiments support sound technical decisions.
- Classification for customer, document or transaction data
- Regression for demand, risk or operational forecasts
- Clustering and dimensionality reduction for exploration
- Feature selection, preprocessing and model evaluation
Ecosystem and tooling
scikit-learn works closely with NumPy, pandas and SciPy, and fits naturally into Python data workflows. Strong specialists also work with Matplotlib or Seaborn for analysis, Jupyter for exploration, and tools such as MLflow, DVC or cloud services for experiment tracking and delivery. The right stack depends on data volume, latency and governance needs.
When to bring in expertise
Companies often need freelance support when a proof of concept must become a reliable service, internal data is difficult to use, or an existing model produces inconsistent results. In Frankfurt, specialists may support finance, logistics, manufacturing, healthcare or commercial teams while working on-site, remotely or in a hybrid setup.
- Turn exploratory notebooks into maintainable pipelines
- Select suitable algorithms and meaningful evaluation measures
- Prepare training data and address leakage or imbalance
- Connect models to APIs, batch jobs or business applications
Skills that matter
A capable scikit-learn professional understands both the library and the problem behind the model. Look for experience with statistics, data quality, feature design, experiment tracking and software testing. Production work may also require Python packaging, containerisation, orchestration, SQL and a clear approach to monitoring model drift.
What strong delivery looks like
Quality work explains why a model was chosen, how its results were validated and where it may fail. Strong professionals create reproducible pipelines, document assumptions and establish a baseline before adding complexity. They communicate model limitations clearly and leave behind code, tests and handover material that another team can operate.
Frequently asked questions
Not sure where to start with scikit-learn? These answers cover the essentials.
scikit-learn is used to build and evaluate machine learning models for tasks such as classification, regression, clustering and anomaly detection. It is especially useful for structured data and for creating repeatable Python workflows from preprocessing through model evaluation.
scikit-learn is usually the practical choice for classical machine learning on structured data, while TensorFlow and PyTorch are commonly selected for deep learning, custom neural networks and large unstructured datasets. The choice depends on the problem, data type, deployment needs and available expertise.
A strong scikit-learn specialist should also understand Python, pandas, NumPy, SQL, statistics and data visualisation. For production work, experience with APIs, testing, containers, experiment tracking and model monitoring is valuable.
The right level depends on the project scope rather than the library alone. A simple analysis may need focused support, while a production system requires a scikit-learn professional who can handle data quality, validation, deployment, documentation and ongoing monitoring.
scikit-learn work is well suited to remote collaboration when data access, documentation and communication are organised. A Frankfurt-based team can combine remote delivery with on-site workshops where domain knowledge, stakeholder alignment or secure infrastructure makes face-to-face work useful.
scikit-learn may not be the best fit for highly specialised deep learning, intensive GPU workloads or very large distributed training jobs. In those cases, teams may consider PyTorch, TensorFlow or distributed data-processing tools alongside a suitable deployment design.
Ask how the professional prevents data leakage, selects evaluation metrics and compares the model with a simple baseline. High-quality scikit-learn work includes reproducible pipelines, clear validation, explainable assumptions and evidence that the result meets the actual business requirement.
scikit-learn can support both exploratory prototypes and production systems when the surrounding workflow is designed carefully. A specialist should be able to move beyond a notebook by packaging preprocessing and prediction logic, adding tests, documenting dependencies and defining how the model will be monitored.
The average hourly rate of freelancers in Frankfurt, Germany who have used scikit-learn in their recent projects is 97 €, which corresponds to a daily rate of about 778 € based on an 8-hour working day.
Of the freelancers in Frankfurt, Germany who have used scikit-learn in their recent projects, 100% hold at least a Bachelor's degree, 91% hold at least a Master's degree, and 27% hold a doctorate.
On average, freelancers in Frankfurt, Germany who have used scikit-learn in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Frankfurt, Germany who have used scikit-learn in their recent projects are German (100%), English (100%), and French (18%).
The most common industries among freelancers in Frankfurt, Germany who have used scikit-learn in their recent projects are Information Technology (82%), Automotive (55%), and Banking and Finance (55%).
The most common business areas among freelancers in Frankfurt, Germany who have used scikit-learn in their recent projects are Information Technology (100%), Business Intelligence (82%), and Product Development (82%).
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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Philipp Thomaschewski
FRATCH CEO
