scikit-learn Experts in Berlin
to turn data into reliable models, matched in minutes with vetted freelancersHire experts who build predictive models, classification pipelines and production-ready machine learning workflows with scikit-learn, pandas and NumPy. FRATCH matches you quickly and precisely with vetted, available freelancers suited to your project.
Meet FRATCH Experts in Berlin, who have recently used scikit-learn
Dmitry P.
Last position:
Freelance Digital Marketing Analyst at Freelance
- Marketing Strategy: Lead the end-to-end analysis and evaluation of cross-channel marketing campaigns across the entire Customer Journey. My focus is identifying optimization potential and deriving clear, actionable recommendations that drive measurable business impact.
- Data Science & AI: Advanced predictive modeling (Churn, LTV), market basket analysis, clustering, and real-time AI-powered audience discovery utilizing RAG/LLMs.
- Marketing Analytics & Measurement: End-to-end attribution analysis, Marketing Mix Modeling (MMM), audience segmentation, conversion path analysis, and A/B testing across all major platforms.
- Data Engineering & Reporting: Designing and managing robust, multi-platform data pipelines (BigQuery, GCP) for data consolidation, automated dashboard generation, and critical API integrations.
Nikolai G.
Last position:
Clinical Data Manager at Dr. Falk Pharma
- Used OpenCode and AI-assisted software engineering to design, implement, refactor, test, and document an end-to-end RAW/SDTM/ADaM pipeline in R for Dr. Falk Pharma (07/2026), including metadata-driven transformations, automated validation rules and QC, traceability, and reproducible clinical outputs.
Deepak M.
Last position:
Lead ML Platform Engineer at Billie GmbH
- Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
- Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
- Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
- Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
- Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
- Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
- Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
- Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
- Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
- Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices
Haseeb Z.
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Sejal V.
Last position:
Data & ML Engineering at Consulting
- Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
- Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
- Exploring Agentic AI & LLM-based tooling for production readiness patterns
Michael B.
Last position:
Product Analytics Consultant - Trust & Safety at Kleinanzeigen
- Detecting fraud patterns by implementing aggressive anti-fraud rules while maintaining acceptable false positive rates, reducing fraud exposure to users by up to 80%
- Supporting ideation and roll-out of new trust and safety features to block fraudulent activity and increase user awareness for fraud
- Supporting Product, Development and Customer Support with BI reports and further guidance to identify and fight fraud and policy violations
Wolfram K.
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
- Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
- Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
- Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
- Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow
Muzamal A.
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Hamza K.
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Enrico G.
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- Lecturer for the GenAI Track at the Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation
Mark W.
Last position:
Independent IT/AI Consultant at Freelance
- IT consulting, coaching, and implementation with a focus on AI
Mathias W.
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
Santina W.
Last position:
Business Analyst & BI Strategist - Comparison Portal at dataweys (self-employed)
- Assessment of the existing reporting landscape and strategic bundling of needs
- Migration and consolidation of reports to Metabase, connected to ClickHouse as the data foundation
- Building and maintaining data pipelines
Stack: Metabase · ClickHouse · Appsmith · Airflow
Nino S.
Last position:
Freelancer in Data Science at International Companies
Proceeding what was started in 10/2023, offering data science development skills fulltime to international clients
Helping companies learn more about their existing (unstructured) data, optimize processes and technical systems, and derive solutions for their problems
Tools and technology used: Python (sklearn, pandas, numpy, Django, sqlAlchemy, pyTorch), Matlab, Docker, AWS EC2, Lambda, S3, SQL, MySQL, Hadoop & Spark, Machine Learning, DNN, AI, Jira, Confluence, Git, CI/CD, GitLab, Jenkins
Louis G.
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
- Work with customers to understand their challenges and provide the best solutions based on open-source data products
- Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
- Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
- Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
- Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
- Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
- Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible
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
1.9 years
Positions per freelancer
7 (Germany: 8)
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
98% (Germany: 99%)
Master's degree or higher
78% (Germany: 83%)
Doctorate
30% (Germany: 21%)
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
91% (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 Berlin 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 Berlin 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 (85%)
- Education (53%)
- Healthcare (45%)
- Professional Services (43%)
- Media and Entertainment (28%)
- Banking and Finance (23%)
- Government and Administration (21%)
- Retail (21%)
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 tools for data preparation, supervised and unsupervised learning, model evaluation and prediction. Companies use it to build models for classification, regression, clustering, recommendation support and anomaly detection without creating algorithms from scratch.
Core capabilities
The library covers linear and logistic regression, decision trees, random forests, gradient boosting, support vector machines, nearest-neighbour methods and clustering. Its pipelines connect preprocessing, feature selection and model fitting into repeatable workflows. Strong specialists also handle missing values, categorical data, scaling, leakage prevention and cross-validation.
Ecosystem and tooling
scikit-learn works closely with Python data tools such as NumPy, pandas and SciPy, and commonly integrates with Jupyter, Matplotlib and joblib. Professionals may connect trained workflows to FastAPI services, cloud storage, MLflow tracking or containerised applications. Knowledge of SQL, Git, testing and deployment helps move a model from an experiment into a maintainable system.
Typical project work
- Prepare and validate datasets for modelling
- Select features and compare suitable algorithms
- Create reproducible training and evaluation pipelines
- Export models for batch or real-time predictions
- Monitor model behaviour after release
Berlin companies in finance, commerce, logistics, mobility and research may bring in freelance expertise when internal teams need a focused modelling capability or a dependable handover. Remote collaboration works well when data access, documentation and review processes are clearly organised.
When to hire a specialist
Bring in a scikit-learn specialist when a proof of concept must become a trusted decision tool, when model results are difficult to reproduce, or when data preparation is undermining performance. Freelancers can audit an existing pipeline, establish evaluation criteria, compare a baseline with more complex approaches and document the reasoning behind the final choice.
Signs of quality
Strong professionals select methods based on the data and business requirement rather than chasing complexity. They separate training and test data correctly, explain trade-offs, use suitable metrics and test the complete pipeline. They also communicate assumptions, identify bias and data drift risks, and leave behind readable code, documented dependencies and a practical path to maintenance.
Frequently asked questions
The facts hiring teams ask for most often when it comes to scikit-learn.
scikit-learn is used to prepare data, train machine learning models and evaluate predictions in Python. Common applications include customer or document classification, demand forecasting, risk assessment, clustering and anomaly detection.
scikit-learn is usually a strong choice for structured or tabular data and conventional machine learning methods. TensorFlow and PyTorch are better suited to deep learning, such as image, audio or large language model workloads, although a project may use several tools together.
A capable scikit-learn specialist should be comfortable with Python, pandas, NumPy, SQL and statistical evaluation. Experience with data versioning, APIs, containers, experiment tracking and cloud deployment is valuable when the model must operate in a production system.
The right level depends on the risk, data quality and delivery stage rather than on a fixed amount of experience. A small modelling task may need focused library knowledge, while a regulated or business-critical system calls for a professional who has handled validation, explainability, deployment and monitoring.
Yes. scikit-learn projects are often suitable for remote collaboration when secure data access, version control and review routines are in place. For Berlin teams, on-site workshops can still help with domain discovery, stakeholder alignment and handover, while English is common and German may matter for local communication.
Ask the specialist to explain the baseline, data split, chosen metrics and reasons for selecting the final model. High-quality scikit-learn work includes reproducible pipelines, tests, clear documentation and an honest account of uncertainty, limitations and likely failure cases.
scikit-learn is often preferable when proven algorithms, transparent workflows and fast iteration matter more than specialised deep learning. Its consistent API reduces unnecessary custom code, while a custom approach may be justified by unusual data types, strict latency needs or algorithms outside its scope.
A scikit-learn professional should clarify the decision the model will support, the available data, the target metric and how predictions will be used. They should also confirm access controls, delivery expectations, integration requirements, ownership of the trained model and who will maintain it after handover.
The average hourly rate of freelancers in Berlin, Germany who have used scikit-learn in their recent projects is 87 €, which corresponds to a daily rate of about 698 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used scikit-learn in their recent projects, 98% hold at least a Bachelor's degree, 78% hold at least a Master's degree, and 30% hold a doctorate.
On average, freelancers in Berlin, Germany who have used scikit-learn in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Berlin, Germany who have used scikit-learn in their recent projects are English (98%), German (91%), and French (15%).
The most common industries among freelancers in Berlin, Germany who have used scikit-learn in their recent projects are Information Technology (85%), Education (53%), and Healthcare (45%).
The most common business areas among freelancers in Berlin, Germany who have used scikit-learn in their recent projects are Information Technology (89%), Research and Development (83%), and Product Development (81%).
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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Stuttgart 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
