scikit-learn Experts in Stuttgart
matched in minutes from over 15,000 CVs with the power of AI.Hire experts who build reliable classification, regression, clustering, and model evaluation workflows with scikit-learn, sklearn, pandas, NumPy, and clean Python pipelines. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Stuttgart, who have recently used scikit-learn
Karin Albiez
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Talha Erciyes
Last position:
Interim Senior Finance Business Partner at SharkNinja Europe Ltd.
Responsibility for commercial finance in Central Europe (DACH and Poland), reporting to the EMEA Commercial Finance Director. Monthly financial reporting, forecasting, and variance analysis, evaluation of promotions and special campaigns, management of planning processes including budgeting, as well as preparation of QBR materials up to CFO level. Took over functional leadership in the finance team after the mandate holder was unavailable.
Noushiq Mohammed K A N
Last position:
Projects at Institute for Intelligent Systems
- Evaluation and analysis of camera-based traffic light and sign recognition system on various LLM-based autonomous driving systems (LMDrive, BEVDriver)
- Implemented VLM based traffic notice instruction generation unit for closed-loop autonomous driving system which alerts driver in unforeseen driving incidents
- Developed independent LLM-based local chatbot with Llama, DeepSeek and Qwen including MLflow evaluation framework
Chaima Dahri
Last position:
Data Scientist Intern at Marelli Automotive Lighting
- Developed and deployed a deep learning model for automated keypoint detection in headlamp light distributions.
- Prepared and processed datasets, and selected VGG16 after benchmarking CNN architectures for the best accuracy efficiency trade-off.
- Delivered a Flask REST API, containerized with Docker, and integrated the solution into an existing internal system, enabling automated and efficient evaluation of headlamp designs.
Akshata Noganihal
Last position:
Data Science Intern at Unified Mentor
- Improved predictive model accuracy by 18% using advanced feature engineering.
- Automated data pipelines via Python ETL, reducing manual work by 25%.
- Documented data flows to identify automation potential and support digitalization projects.
Divij Wadhawan
Last position:
Data Scientist at Daimler R&D, Daimler AG
- Mercedes Me is an app that connects your phone to several features in the car
- Implemented analytical KPIs for the Digital Drivers Log (Fahrtenbuch) feature
- Used PySpark on Databricks
Bhavin Moriya
Last position:
Research Assistant at Hochschule Esslingen
- Working on the AnoMoB project, applying homomorphic encryption to extract insights from encrypted mobility data.
- Explored CKKS and TFHE schemes, multi-party computation, oblivious transfer, and proxy re-encryption.
- Implemented encrypted comparison and homomorphic operations on complex numbers using CKKS.
- Homomorphic encryption with OpenFHE & TFHE-rs (Rust), SQL analysis, ML (Pandas/Polars/Scikit-learn)
Discover over 15,000 top freelancers
Statistics of experts using scikit-learn
Aggregated from the professional profiles of matched freelancers.
Experience
10 years (Germany: 11 years)
Position duration
1.6 years (Germany: 1.9 years)
Positions per freelancer
8
Top business areas
Business Intelligence, Information Technology, Product Development
Top industries
Automotive, Information Technology, Manufacturing
Certification focus areas
Business Intelligence, Logistics, Research and Development
Bachelor's degree or higher
100% (Germany: 99%)
Master's degree or higher
71% (Germany: 83%)
Doctorate
14% (Germany: 20%)
Certifications per freelancer
2
Most common languages
German, English, Hindi
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 Stuttgart 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 Stuttgart 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
What it is
scikit-learn is a Python library for classic machine learning. It is used to train, test, and compare models for tabular data, text features, and many forecasting and classification tasks. Teams choose it when they want clear workflows, strong evaluation, and simple handoff in Python.
Common work
- Build classification and regression models
- Create preprocessing and feature engineering pipelines
- Run clustering, anomaly checks, and model selection
- Validate models with cross-validation and metrics
- Package reusable training and inference code
Stack around it
Strong specialists use scikit-learn together with pandas, NumPy, SciPy, and joblib. They know how to combine transformers, estimators, and pipelines without leaking data between training and testing. They also understand when a simple model is better than a complex one.
When companies need help
Bring in outside experts when notebooks need to become maintainable code, when model results are hard to trust, or when old training scripts are difficult to extend. In Stuttgart, this often matters for industrial, automotive, and engineering teams that need clear Python workflows and practical delivery.
What good specialists do
A good specialist does more than fit a model. They define the target, check the data, choose a baseline, tune only where it matters, and explain trade-offs in plain language.
- Writes clean, testable Python
- Uses the right metrics for the problem
- Spots data leakage and weak features
- Documents assumptions and limits
- Hands over code that others can maintain
Collaboration style
scikit-learn work fits both remote and on-site setups. For Stuttgart teams, German or English collaboration may both work depending on the internal group, data access, and review process. The best freelancers adapt to the team’s pace and keep the work easy to verify.
Frequently asked questions
Key details about scikit-learn, drawn from the questions we get asked most.
scikit-learn is used for practical machine learning in Python, especially on structured data. Companies use it for classification, regression, clustering, feature preprocessing, and model evaluation. It fits projects where clear logic and repeatable results matter more than heavy custom research code.
scikit-learn is usually the better choice for classic machine learning on tabular data and for fast, explainable baselines. TensorFlow and PyTorch are more common for deep learning and custom neural networks. If your project needs pipelines, metrics, and simpler maintenance, scikit-learn often wins.
A strong scikit-learn specialist usually also knows pandas, NumPy, SciPy, and Python packaging. They should understand feature engineering, cross-validation, model metrics, and data cleaning. For production work, joblib, APIs, and testing practices are also important.
A scikit-learn project can start with a solid generalist, but harder work needs someone who has handled real data issues before. If the task includes leakage risks, imbalanced classes, or fragile training code, look for deeper hands-on experience. The right level depends on how stable the data and requirements are.
Yes, scikit-learn work is often remote-friendly because most of the work happens in Python, notebooks, and code review. Stuttgart teams may still want on-site sessions for sensitive data, stakeholder workshops, or handover meetings. Good freelancers can work in either setup if access and review steps are clear.
Look for clear pipelines, correct validation, and sensible baseline models in the first review of scikit-learn work. Strong experts explain why a model is chosen, how metrics were selected, and where the limits are. They also leave code that another specialist can read and extend.
scikit-learn is a good fit when you need dependable Python models with simple training and inference flows. It works well in production when the surrounding code handles versioning, testing, and monitoring. For very large deep learning systems, another stack may be a better fit.
Before bringing in a scikit-learn specialist, prepare sample data, the target outcome, and any known business rules. It also helps to share how the model will be used, who will review it, and whether the work must fit an existing Python codebase. That makes the first sprint more focused.
The average hourly rate of freelancers in Stuttgart, Germany who have used scikit-learn in their recent projects is 82 €, which corresponds to a daily rate of about 657 € based on an 8-hour working day.
Of the freelancers in Stuttgart, Germany who have used scikit-learn in their recent projects, 100% hold at least a Bachelor's degree, 71% hold at least a Master's degree, and 14% hold a doctorate.
On average, freelancers in Stuttgart, Germany who have used scikit-learn in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Stuttgart, Germany who have used scikit-learn in their recent projects are German (100%), English (100%), and Hindi (43%).
The most common industries among freelancers in Stuttgart, Germany who have used scikit-learn in their recent projects are Automotive (71%), Information Technology (57%), and Manufacturing (57%).
The most common business areas among freelancers in Stuttgart, Germany who have used scikit-learn in their recent projects are Business Intelligence (71%), Information Technology (71%), and Product Development (71%).
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 
Munich 
Cologne 
Frankfurt 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
