scikit-learn Experts in Germany
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Meet FRATCH Experts in Germany, 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.
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.
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Daryoosh Dehestani
Last position:
FP&A Data & AI Architect at Epta Group
Scope: Embedded as FP&A Data & AI Architect within the Finance function of a major European refrigeration manufacturer, leading the transformation of manual, fragmented financial reporting into an automated, governance-driven intelligence platform. Driving the shift from Excel-based controlling to structured data architecture, Power BI analytics, and AI-assisted financial operations.
Financial Data Integrity & ERP Governance
- Initiated and led GL vs. subledger reconciliation investigations, identifying and resolving structural mismatches between General Ledger and subledger data that had gone undetected prior to engagement
- Conducted asset analysis to identify items missing from General Ledger postings, surfacing gaps in fixed asset tracking and period-end completeness
- Validated SAP reports, establishing baseline data quality standards for Finance team consumption
- Established systematic SAP data validation framework ensuring ongoing integrity between ERP postings and downstream reporting outputs
Finance Reporting Transformation
- Designed and implemented a structured Transformation Project approach for converting manual Finance reports into fully automated processes
- Created and owns the Data Reporting Audit Log; a centralized tracking system capturing report owners, stakeholders, data sources, manual effort estimates, and automation opportunity scores across the Finance function
- Mapped the full reporting landscape identifying quick-win automation targets and strategic Power BI migration candidates
- Actively reducing manual Excel and PowerPoint dependency across FP&A workflows; replacing point-in-time snapshots with live, governed data models
Power BI & Analytics Enablement
- Introduced and presented Power BI as the strategic reporting platform to Finance leadership, building internal buy-in for the BI transformation roadmap
- Designed initial Power BI architecture aligned with SAP, Salesforce and Oracle data structures and FP&A reporting requirements
- Established report ownership, governance documentation, and data lineage standards enabling sustainable self-service analytics across the Finance team
Transformation Infrastructure & Collaboration
- Configured and deployed Jira as the transformation project management hub, establishing structured sprint workflows, backlog management, and progress visibility for Finance IT initiatives
- Proposed and initiated a dedicated FP&A Communication & Transformation Hub, a structured cross-functional forum aligning Finance, IT, and business stakeholders around the reporting transformation roadmap
- Positioned the Finance function as an active driver of data governance and digital transformation within the broader organization
Outcomes
- GL/subledger reconciliation gaps identified and investigation framework established within first two weeks of engagement
- Data Reporting Audit Log deployed; first structured inventory of Finance reporting landscape in company history
- Power BI transformation roadmap presented and approved by Finance leadership
- Jira-based project governance live; Finance transformation now tracked with full sprint visibility
Technologies: SAP FI/CO · Power BI · DAX · SQL · Excel (advanced) · Power Query (M) · Power Automate · VBA · Jira · Microsoft 365 · SharePoint · Salesforce (Sales Data) · Oracle HCM · Python
Kiriakos Krastillis
Last position:
Tech Lead / Architect : OTTO API Platform at OTTO
maturing their API Practices on both, a Business and Technology level. My role encompasses strategy, architecture, developer advocacy as well as hands on software engineering, enabling both technical teams and business leadership to adopt and act on API- centric principles effectively. Coincidentally, we also establish GitOps, DX and Platform Best practices with this project.
Highlights:
- Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
- Formulating a way forward for API Lifecycle Management at OTTO
- Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals
API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, react, nodejs, typescript, redocly, reactive programming, CDC, golang, gingonic, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.
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.
Anjaneya Marimireddygari
Last position:
Machine Learning Engineer Intern at Slash Mark
- Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
- Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
- Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
- Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
- Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.
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
Danny-Michael Busch
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
Giuseppe Abrignani
Last position:
Embedded Software Developer at Inheco
- AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
- Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
- Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
- Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.
Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps
Ashwin Parthasarathy
Last position:
Freelance Data Scientist at Mercor Intelligence
- Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
- Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
- Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.
Khaled Teilab
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
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Shanna Tellaev
Last position:
Problem Resolution Manager at CARIAD SE (VW AG), formerly CARMEQ GmbH (VW AG)
- Automotive SPICE®: all assessments fully achieved
- Agile transformation: V-model → SAFe successfully implemented
- Series release: on-time, quality-assured software delivery for key Volkswagen Group models (including ECE homologation)
- Stakeholder management: internal & external
- Process optimization: implemented a continuous improvement process (CIP) with a tracking system
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.
Discover over 15,000 top freelancers
Statistics of experts using scikit-learn
Aggregated from the professional profiles of matched freelancers.
Experience
11 years
Position duration
1.9 years
Positions per freelancer
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
99%
Master's degree or higher
83%
Doctorate
20%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
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 Germany 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 Germany 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 does
scikit-learn is a Python library for classic machine learning. Teams use it to turn structured data into models for prediction, segmentation, ranking, and anomaly detection. It fits well when the goal is clear, reliable modeling rather than large-scale deep learning.
Common work
- Build classification and regression models
- Cluster customers, cases, or documents
- Compare features and tune parameters
- Validate pipelines and metrics
- Package models for repeatable use
Tooling around it
Strong specialists know NumPy, pandas, SciPy, and Jupyter, plus the habits that keep experiments clean. They also understand train-test splits, cross-validation, feature engineering, preprocessing, and how sklearn pipelines prevent fragile workflows.
When to bring in help
Companies look for freelance expertise when a data product needs a dependable baseline, an existing model needs repair, or a team wants cleaner evaluation. In Germany, this often comes up in manufacturing, insurance, retail, logistics, and B2B analytics, where structured data is common.
What strong experts do
A strong specialist explains model choice in plain language, not jargon. They can compare linear models, tree-based methods, support vector machines, and ensemble methods, then choose what fits the data, the latency needs, and the review process.
Remote or on-site
scikit-learn work is often done remotely because most tasks depend on data access, notebooks, and code review. On-site collaboration can help with sensitive data, stakeholder workshops, or handover sessions. For Germany-based teams, clear communication in English is usually enough, though local language can help in cross-functional settings.
Frequently asked questions
Questions about scikit-learn? Start with the answers below.
scikit-learn is used to build classic machine learning workflows in Python. Companies use it for prediction, customer grouping, fraud flags, forecasting support, and model validation on structured data. It is a good fit when the task needs reliable, explainable methods rather than deep neural networks.
Yes, scikit-learn and sklearn refer to the same library. Sklearn is the import name used in code, while scikit-learn is the full project name. When you hire a specialist, the important point is whether they can design solid pipelines, preprocessing, and evaluation around it.
scikit-learn is usually the better choice for tabular data, fast prototyping, and classic supervised or unsupervised methods. TensorFlow and PyTorch are stronger when the project needs deep learning, custom neural networks, or more control over training loops. Many teams use scikit-learn first for baselines and benchmarking.
A strong scikit-learn specialist usually works comfortably with pandas, NumPy, and data cleaning. They should also understand feature engineering, model selection, metrics, and how to avoid leakage in training data. Experience with Jupyter, Git, and basic Python packaging is often useful too.
A simple scikit-learn proof of concept may need only one focused specialist who can set up preprocessing, fit a baseline model, and explain the results. Production work needs more depth, especially if the model must be repeatable, monitored, or handed over to a team. The key is not years on paper, but evidence of finished ML work.
Yes, scikit-learn work is often remote because it is code, data, and review driven. For Germany-based teams, remote collaboration works well when data access, security rules, and meeting times are clear. On-site time is mainly useful for sensitive data, workshops, or handover sessions.
A strong scikit-learn expert can explain why a model was chosen, how it was validated, and what its limits are. Look for clean pipelines, sensible metrics, and careful handling of missing data and leakage. Good specialists also document assumptions so the work can be reused.
Typical scikit-learn deliverables include notebooks, reusable Python code, trained model pipelines, evaluation reports, and feature preparation logic. In some projects, the specialist also hands over clear guidance for retraining or integration into another system. The best work is easy for your team to understand and continue.
The average hourly rate of freelancers in Germany who have used scikit-learn in their recent projects is 85 €, which corresponds to a daily rate of about 679 € based on an 8-hour working day.
Of the freelancers in Germany who have used scikit-learn in their recent projects, 99% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 20% hold a doctorate.
On average, freelancers in Germany who have used scikit-learn in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Germany who have used scikit-learn in their recent projects are English (99%), German (96%), and French (18%).
The most common industries among freelancers in Germany who have used scikit-learn in their recent projects are Information Technology (77%), Education (55%), and Healthcare (33%).
The most common business areas among freelancers in Germany who have used scikit-learn in their recent projects are Information Technology (90%), Research and Development (77%), and Product Development (76%).
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 
Stuttgart 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
