scikit-learn Expert
for reliable machine learning solutions, matched in minutes with the power of AIHire experts who build classification, regression and clustering solutions with scikit-learn, pandas and NumPy. Work with vetted, available freelancers matched to your project needs quickly and precisely.
Meet FRATCH Experts who have recently used scikit-learn
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Peter S.
Last position:
Senior ML Engineer & AI Researcher at Anonymous Client
Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing
- Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
- Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
- Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.
Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Kiriakos K.
Last position:
Tech Lead / Architect : OTTO API Platform at OTTO
Maturing their API practices on both a business and technology level. My role covers 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, Node.js, TypeScript, Redocly, reactive programming, CDC, Golang, Gin, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.
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.
Michael N.
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Karin A.
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.
Shanna T.
Last position:
Freelance Data Scientist & AI Developer at tellaev.de
- Portfolio development & customer acquisition
- Portfolio development (RAG, NLP fine-tuning, process automation with n8n) and active customer acquisition
- Positioning: GDPR-compliant, locally hosted AI solutions for SMEs
Daryoosh D.
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
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.
Talha E.
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.
Bora D.
Last position:
Software Architect at DZ HYP AG
- Lead architect for an enterprise loan digitisation programme, coordinating 12 engineers across five workstreams and serving as final technical authority on system design.
- Cut critical application response times by 68% (display 17.3s → 5.6s; modification 11.8s → 5.0s) through targeted caching, OData query optimisation and lazy-loading refactoring; further optimisation in progress.
- Own production error triage, prioritisation and resolution across a multi-application portfolio supporting live lending operations.
- Design and implement SAP Fiori applications on SAP UI5, TypeScript and RAP, owning delivery from architecture and code through rollout and production support.
- Established C4 architecture documentation and decision records for the full programme, enabling faster onboarding and consistent cross-workstream design governance.
- Co-managed S/4HANA release cycle alongside primary responsibilities, coordinating directly with SAP support to resolve critical system issues across the portfolio.
Samuel K.
Last position:
Founder & Agentic AI Engineer at Agentakt LLC
Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.
Selected client engagement: Scalutions
Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.
Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.
Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.
Anjaneya M.
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 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
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
21%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
98%
Based on our profile pool as of 26 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology 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 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 26 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 (77%)
- Education (55%)
- Healthcare (34%)
- Professional Services (33%)
- Automotive (30%)
- Banking and Finance (29%)
- Manufacturing (29%)
- 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 APIs for preparing data, training models, evaluating results and applying predictions. Companies use it to turn structured data into repeatable classification, regression, clustering and anomaly-detection workflows.
Core capabilities
The library covers supervised and unsupervised learning, feature selection, dimensionality reduction and model evaluation. Its estimators support common algorithms such as linear models, decision trees, random forests, gradient boosting, support vector machines and nearest-neighbor methods. Pipelines help keep preprocessing and prediction steps consistent.
Ecosystem and tooling
Strong specialists usually work across the Python data ecosystem. They combine scikit-learn with pandas for tabular data, NumPy for numerical operations, SciPy for scientific computing and Matplotlib or Seaborn for analysis. They may also use Jupyter, SQL, MLflow, joblib, Docker and cloud services to move a model from exploration into a maintainable service.
Typical project work
- Prepare, clean and validate structured datasets
- Build classification and regression models
- Compare algorithms with suitable evaluation methods
- Create reusable preprocessing and modeling pipelines
- Package models for batch or API-based predictions
Projects often include customer segmentation, demand forecasting, risk assessment, recommendation features and operational decision support. The right approach depends on data quality, business costs and the need for explainable results.
When expertise matters
Companies bring in freelance expertise when internal teams need a reliable model quickly, lack specialist machine learning capacity or must improve an existing prototype. Specialist support is valuable when data leakage, class imbalance, weak validation or inconsistent preprocessing could undermine results. Clear requirements, accessible data and a defined delivery environment make collaboration more effective.
Signs of strong specialists
- They choose metrics that reflect the business decision
- They separate training, validation and test data correctly
- They compare a transparent baseline with more complex models
- They explain feature preparation and model limitations
- They document reproducible training and deployment steps
Experienced professionals treat model quality as more than a score. They test robustness, monitor changes in incoming data and make assumptions visible. They also know when scikit-learn is sufficient and when a deep learning or specialized library is more appropriate.
Frequently asked questions
Not sure where to start with scikit-learn? These answers cover the essentials.
scikit-learn is used to build machine learning workflows for structured data. Common applications include classification, regression, clustering, forecasting support and anomaly detection, from exploratory analysis through model evaluation and prediction.
scikit-learn is usually a strong choice for tabular data and classical machine learning because its APIs are compact and its models are easy to compare. TensorFlow and PyTorch are more suited to deep learning, custom neural networks and large unstructured inputs such as images, audio or text.
A strong scikit-learn specialist should understand Python, pandas, NumPy, SQL and data visualization. Knowledge of statistics, feature engineering, experiment design, model serving and tools such as Docker or MLflow is also useful for production work.
The required experience depends on the data, risk and delivery scope rather than a fixed duration. A simple model assessment may need focused support, while a production workflow requires expertise in validation, monitoring, reproducibility, deployment and communication with domain teams.
Yes. scikit-learn projects are often suitable for remote collaboration when data access, environments and documentation are well organized. On-site work can help when specialists need close contact with operational teams, sensitive data processes or complex domain decisions.
Ask how the specialist would define the target, select a baseline, prevent leakage and choose evaluation metrics. Strong scikit-learn professionals can explain trade-offs clearly, show reproducible workflows and connect model behavior to the business decision.
scikit-learn can support production systems when models are packaged, versioned and monitored properly. A specialist should address input validation, pipeline consistency, retraining, dependency management, latency and what happens when incoming data changes.
Before using scikit-learn, freelancers should clarify the business objective, available labels, data permissions, prediction timing and acceptable errors. They should also confirm how the result will be consumed, who owns deployment and which language or documentation expectations apply.
The average hourly rate of freelancers who have used scikit-learn in their recent projects is 84 €, which corresponds to a daily rate of about 672 € based on an 8-hour working day.
Of the freelancers 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 21% hold a doctorate.
On average, freelancers 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 who have used scikit-learn in their recent projects are English (99%), German (97%), and French (17%).
The most common industries among freelancers who have used scikit-learn in their recent projects are Information Technology (77%), Education (55%), and Healthcare (34%).
The most common business areas among freelancers who have used scikit-learn in their recent projects are Information Technology (90%), Research and Development (77%), and Product Development (77%).
Main locations of FRATCH Experts, who have recently used scikit-learn
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Hamburg 
Munich 
Cologne 
Frankfurt 
Stuttgart 
Nuremberg In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Vienna Graz Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Zurich Geneva Basel Bern Countries:
Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
