scikit-learn Experts in Berlin
in minutes from over 15,000 CVs with the power of AI.Hire experts who turn scikit-learn into working models, clear evaluation pipelines, and reliable feature engineering. They help with sklearn workflows, model selection, and production-ready Python ML projects, matched fast and precisely from vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used scikit-learn
Dmitry Pankov
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.
Deepak Mishra
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 Zahid
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 Vaidya
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 Bader
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 Knan
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 Ali
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 Khan
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 Goerlitz
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
Mathias Wilhelm
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 Wey
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
Louis Guitton
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
Nino Sandmeier
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
Muhammad Latif
Last position:
AI Product Intelligence SaaS Platform at ProductLogik
- Defined product vision, roadmap, and subscription-based monetization model.
- Architected multimodel AI orchestration (Gemini + GPT fallback) ensuring reliability and cost efficiency.
- Designed explainable insight engine with confidence scoring and agile antipattern detection.
- Built and deployed full-stack architecture (FastAPI, PostgreSQL, React) with secure authentication and quota governance.
- Tech: Python, FastAPI, PostgreSQL, React, TypeScript, Stripe, Gemini API, OpenAI API.
Ashwin Parthasarathy
Last position:
Data Scientist at Mercor Intelligence
- Elevated LLM output reliability by engineering domain-specific prompts and evaluation logic, improving reasoning consistency across production language model workflows.
- Designed advanced coding benchmarks and validated solutions to strengthen training and evaluation datasets, improving model performance on technical problem-solving tasks.
- Designed and implemented automated evaluation frameworks for technical reasoning tasks; optimized LLM output reliability by 15% through rigorous prompt engineering and rubric-based benchmarking.
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, Product Development
Bachelor's degree or higher
98% (Germany: 99%)
Master's degree or higher
78% (Germany: 83%)
Doctorate
29% (Germany: 20%)
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 30 Aug 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 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 classical machine learning. It is used to train, tune, and evaluate models for classification, regression, clustering, and dimensionality reduction. Many teams call it sklearn in day-to-day work.
Common uses
- Build predictive models from tabular data
- Create repeatable preprocessing and feature pipelines
- Compare baselines before deeper modeling work
- Run cross-validation and model selection
- Package experiments into maintainable Python code
Ecosystem fit
Strong specialists use scikit-learn with NumPy, pandas, SciPy, and Jupyter. They know how to combine estimators, transformers, and pipelines without making the code hard to maintain. When needed, they also connect it to joblib, MLflow, or API layers used in Python systems.
When to bring in help
Companies bring in freelance expertise when a model needs to be built quickly, cleaned up, or made easier to trust. This is common in product analytics, forecasting, risk scoring, and internal automation. In Berlin, it often fits teams that want Python-based ML support without adding a full-time specialist.
What good work looks like
Good scikit-learn professionals start with the data, not the algorithm. They choose simple models first, avoid leakage, document preprocessing, and explain why a result is stable or weak. They also write code that another specialist can read and extend later.
Typical delivery
A strong engagement often includes:
- A baseline model with clear metrics
- A reusable preprocessing pipeline
- Notes on feature choice and limits
- Guidance for handoff to engineering or analytics teams
- A clean path for future sklearn updates
Frequently asked questions
The facts hiring teams ask for most often when it comes to scikit-learn.
scikit-learn is used to build classic machine learning workflows in Python. Companies use it for prediction, classification, clustering, anomaly detection, and feature preprocessing. It is a good fit when the team needs reliable models and clear evaluation rather than heavy custom research code.
scikit-learn is usually the better choice for structured data, baselines, and fast iteration on traditional ML tasks. TensorFlow and PyTorch are more common for deep learning and complex neural networks. If your project is about tabular data, explainable models, or standard ML pipelines, sklearn often makes more sense.
Hire scikit-learn help when a model needs to be designed, cleaned up, or reviewed by someone who knows the library well. This is useful if your internal team is busy, the pipeline is fragile, or the model logic must be easy to hand over. It also helps when you need a specialist to debug preprocessing or evaluation issues.
A strong scikit-learn specialist usually works comfortably with Python, NumPy, pandas, and Jupyter. Knowledge of data cleaning, statistics, feature engineering, and version control is also important. For production work, experience with APIs, joblib, and model tracking tools is a plus.
A scikit-learn project can be small or complex depending on the data and the business goal. Simple baseline work needs someone who can move fast and stay disciplined about testing and leakage. More demanding work needs deeper experience with pipelines, validation, and error analysis.
Yes, scikit-learn work is often done remotely because most tasks happen in Python notebooks, code review, and shared data workflows. For Berlin teams, remote collaboration works well if the data access, security rules, and feedback process are clear. On-site sessions can still help early in the project or during stakeholder reviews.
Look for a scikit-learn specialist who explains choices clearly and can show how they prevent data leakage and overfitting. Good work includes clean pipelines, sensible metrics, and a simple baseline before more complex models. The best specialists also make their code easy for another professional to maintain.
Yes, scikit-learn and sklearn refer to the same Python machine learning library. sklearn is the short name people often use in code, discussions, and searches. If you are hiring for either term, you are usually looking for the same kind of specialist.
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 697 € 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 29% 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 (54%), and Healthcare (43%).
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 (80%).
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.
Hamburg 
Munich 
Cologne 
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Stuttgart 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
