scikit-learn Experts in Austria
matched in minutes by AIHire experts who create production-ready models for classification, regression, clustering and forecasting, while connecting scikit-learn with pandas, NumPy, model APIs and cloud workflows. FRATCH matches you quickly and precisely with vetted, available freelance specialists.
Meet FRATCH Experts in Austria, who have recently used scikit-learn
Stefan D.
Last position:
BI Consultant in Controlling at Reutter GmbH
- Extraction, transformation, and cleansing of data from Microsoft Dynamics AX
- Creation of sales reports in Power BI
- Training employees in business intelligence
- Technologies: Power BI, SQL, SQL Server Integration Services (SSIS)
Alexander L.
Last position:
Guest lecturer in Artificial Intelligence (Master’s Level) at FH des BFI Wien
- Teaching & presenting
- Communication
- Effectively communicate complex technical topics to non-technical audiences through lectures
- Guided non-technical students from zero knowledge to confidently understanding and applying algorithms to achieve business outcomes
Fabio G.
Last position:
IT Architect, Requirements Analyst and Consultant at CANCOM
- Supports CANCOM customers in migrating legacy on-prem systems to Microsoft Fabric and Microsoft Foundry
- Takes over and stabilizes existing solutions after a short handover
- Business analysis and requirements engineering for migration to a new cloud environment
- Optimization of machine learning models for feature extraction and customer profiling
- Ensures data protection and compliance
- Leads the migration of on-prem systems to Microsoft Fabric
- Designs new AI platforms for clients
- Tests the integration of chatbots for document intelligence with Microsoft Foundry, including requirements analysis, implementation, validation, and client communication
Kevin L.
Last position:
Data Consultant at VBV Pensions- und Vorsorgekasse Österreich
Development of a structured framework and comprehensive guidelines for documenting business and audit processes in a regulated financial environment. Support in standardizing process documentation to improve transparency, consistency, and traceability across all operational workflows. Contribution to defining documentation standards, templates, and governance principles for internal process management and audit readiness.
Armin F.
Last position:
Head of AI & Data Science at Ascent DACH
- Lead architect for AI and ML projects including GenAI, LLM-based apps and forecasting solutions
- Guided customers through solution scoping, architecture design, and PoCs across various industries (Pharma, Insurance, Logistics, FMCG)
- Delivered production ML pipelines using Azure ML, MLflow, and MLOps best practices
- Responsible for effort estimation, delivery and staffing of 5 – 10 projects simultaneously
- Hiring manager for the data science and AI team and responsible for creating the technological offering and roadmap in the AI & Data Science space
- Built and scaled the AI/Data Science service offering from scratch to a high 6-figure annual revenue with 30+ successful deliveries and 20+ clients
- Regular speaker at AI and data science conferences and academic institutions
Dániel N.
Last position:
Postdoctoral Researcher - Theoretical and Computational Physics at Radboud University
- Built and maintained C and C++ simulation engines with Python analysis for studies of 4D random geometries on shared HPC systems.
- Developed modular Python pipelines with clear interfaces and caching for large datasets to improve analysis throughput and reuse.
- Automated SLURM and PBS batch workflows for submission, monitoring, environment capture, and artifact packaging to ensure reproducibility.
- Refactored utilities into tested, documented packages to lower maintenance effort and support collaboration.
- Supervised BSc students and organized seminars.
- Published several peer-reviewed papers.
Discover over 15,000 top freelancers
Statistics of experts using scikit-learn
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2.5 years
Positions per freelancer
9
Top business areas
Information Technology, Business Intelligence, Research and Development
Top industries
Education, Information Technology, Banking and Finance
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
33%
Certifications per freelancer
2
Most common languages
German, English, Hungarian
Speak two or more languages
100%
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 Austria 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 Austria 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.
- Education (67%)
- Information Technology (67%)
- Banking and Finance (50%)
- Manufacturing (50%)
- Construction (33%)
- Energy (33%)
- Transportation (33%)
- Media and Entertainment (33%)
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 machine learning library for Python. It provides consistent tools for preparing data, training models, validating results and making predictions. Companies use it to turn structured data into practical decision support, automated classification and forecasting workflows.
Models and methods
The library covers supervised and unsupervised learning without forcing teams into one model family. Specialists work with linear models, decision trees, random forests, gradient boosting, support vector machines, nearest neighbours, clustering and dimensionality reduction. They select methods according to the data, target, constraints and business risk.
Data and tooling
Strong scikit-learn work depends on the surrounding Python data stack. Experts commonly combine it with pandas for data preparation, NumPy for numerical operations, SciPy for scientific computing and Matplotlib or Seaborn for analysis. Pipelines, transformers, feature selection, cross-validation and model persistence keep experiments reproducible and easier to operate.
Typical project work
- Prepare and validate datasets for modelling
- Build classification, regression or clustering pipelines
- Compare models with suitable evaluation measures
- Create reusable preprocessing and prediction workflows
- Package models for APIs, batch jobs or internal tools
These deliverables appear in areas such as customer analytics, fraud detection, demand planning, document processing, quality control and recommendation workflows. In Austria, teams across manufacturing, finance, retail and research may need specialists who understand both modelling and the operational setting.
When specialists help
Companies often bring in freelance expertise when a proof of concept must become a dependable service, when internal data teams need support with model selection, or when predictions are unstable in production. Remote collaboration works well for notebook reviews, pipeline design and model evaluation; on-site work can help when data access, domain workshops or operational processes require close coordination. Clear technical English or German can matter for stakeholder alignment.
What strong experts bring
The best professionals do more than call a fitting method. They check leakage, missing values, class imbalance, validation design and feature quality before trusting a score. They explain trade-offs, document assumptions and establish monitoring for drift and changing input data. Experience with deployment, testing, version control and responsible handling of sensitive data helps turn a scikit-learn experiment into a maintainable system.
Frequently asked questions
Questions about scikit-learn? Start with the answers below.
scikit-learn is used to build machine learning workflows for structured data. Common applications include classification, regression, clustering, anomaly detection, feature reduction and predictive forecasting.
scikit-learn is usually the better fit for classical machine learning on tabular data and for fast, explainable experimentation. TensorFlow and PyTorch are more suited to deep learning, custom neural network architectures and large-scale work with images, audio or language.
A strong scikit-learn specialist should also understand Python, pandas, NumPy and SQL. Experience with data visualisation, statistical testing, cloud deployment, APIs, containers and model monitoring is valuable when the work must reach production.
The right level depends on the risk and scope of the work. A focused analysis may need a specialist who can prepare data and compare models, while a production system calls for deeper expertise in validation, testing, deployment and ongoing monitoring.
Yes, many scikit-learn projects can be completed remotely through shared repositories, secure data access, documentation and regular reviews. On-site collaboration may be useful for sensitive datasets, domain workshops or close work with Austrian business and research teams.
Look for clear validation logic, realistic evaluation data and a documented preprocessing pipeline. Good scikit-learn work explains model limits, checks for leakage and bias, and includes a practical plan for deployment, reproducibility and monitoring.
scikit-learn can support production systems when models are packaged, tested and monitored properly. It works well behind APIs or in batch workflows, but the surrounding infrastructure must handle versioning, data changes, reliability and secure access.
A scikit-learn professional should clarify the business objective, prediction target, available data, success criteria and delivery environment. They should also ask about data permissions, latency needs, retraining plans, explainability expectations and who will maintain the workflow after handover.
The average hourly rate of freelancers in Austria who have used scikit-learn in their recent projects is 104 €, which corresponds to a daily rate of about 829 € based on an 8-hour working day.
Of the freelancers in Austria who have used scikit-learn in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Austria who have used scikit-learn in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Austria who have used scikit-learn in their recent projects are German (100%), English (100%), and Hungarian (33%).
The most common industries among freelancers in Austria who have used scikit-learn in their recent projects are Education (67%), Information Technology (67%), and Banking and Finance (50%).
The most common business areas among freelancers in Austria who have used scikit-learn in their recent projects are Information Technology (100%), Business Intelligence (83%), and Research and Development (67%).
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.
Countries:
- Germany
- Austria
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