Text Mining Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Text Mining
Chintan P.
Last position:
Product Owner and Technical Product Lead at Sustamize GmbH
LLM-based features for automated CO₂e data extraction from unstructured documents (70% reduction)
Agentic AI pipeline for automated Scope 3 emissions calculations with 150.000+ validated data records
Intelligent API workflows for real-time carbon footprint calculations in ERP and ESG systems
ML algorithms for predicting emissions hotspots and optimizing product design
Automated data validation pipelines with NLP for quality assurance of CO₂e datasets
Led a 15-person cross-functional team in developing 10+ AI features
Strategic product planning and AI roadmap with 35% shorter time-to-market
Stakeholder management with DAX companies (40% higher satisfaction, 95% retention)
On-time project delivery with 95% budget adherence through data-driven backlog management
Agile methods (Scrum, Kanban) with continuous AI/ML integration (25% increase in team velocity)
Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Dieter R.
Last position:
Driver analyses at Genactis GmbH
- Calculation of attribute importance based on driver analyses
- Interpretation, reporting, and consulting
Valery K.
Last position:
Sr. Data Scientist & Engineer at Virtual Minds
- Development of high-performance ad distribution via auction
- Holistic (multi-campaign & multi-channel) advertisement placement optimization
- Algorithmic optimization for NP-Hard/NP-e
- Multiple Knapsack Problem with constraints
- Online estimation of parameters in stochastic environments
Tools: Python, R, Kotlin, MILP/SAT/CP Solvers, Pytorch, Pandas, Docker
Marco P.
Last position:
Co-founder at Health AI Language Learning Startup
Co-founded an AI-native language learning startup, defining the product vision, AI architecture and technical roadmap. Designed and built the AI and backend stack, including LLM fine-tuning pipelines, custom agentic workflows, and scalable inference infrastructure. First product currently in private beta.
Niko K.
Last position:
Co-founder & AI Engineer at KAIKI GmbH
End-to-end responsibility for all products - concept, architecture, development, and production operation as the sole developer; in addition, customer meetings, proposals, and marketing.
Underwriting Copilot - AI assistant for industrial insurance (in production at customer sites)
- Supports underwriters in analyzing industrial insurance submissions - in production use at an industrial insurer.
- Framework-independent RAG architecture with Hybrid Search (BM25 + pgvector) across large, mixed document sets.
- Two-stage evaluation and observability pipeline (code assertions + LLM-as-Judge) that makes answer quality, retrieval accuracy, and citation integrity measurable in a regression-safe way.
Kaiki Menu Analyzer - Data intelligence platform (in production at customer sites)
- Automatically captures and analyzes menu data from around 25,000 German restaurants.
- Scalable 7-container architecture (FastAPI, partitioned PostgreSQL, Redis/RQ) with LLM-supported extraction of structured data from PDF, HTML, and images.
- Full CI/CD pipelines (GitHub Actions), production cloud deployment, interactive dashboards (Dash).
Kaiki GEO Atlas - GEO platform (in production at customer sites)
- Measures brand visibility across five AI engines (ChatGPT, Gemini, Perplexity, Grok, Claude), each augmented with web search, orchestrated as a DAG workflow pipeline (Dispatcher → Sub-workflows → Scoring → Report) with fail isolation.
- 6-container deployment (FastAPI, Celery, Redis, PostgreSQL); LLM cost estimation, PDF audit report, rule-based cross-signal insights (no extra LLM cost).
Data Pipeline & Analytics Platform - competitive analysis in the automotive aftermarket
- Automated data pipeline with gap analysis algorithms and role-based access control; 230+ tests.
- Backend with FastAPI, PostgreSQL, SQLAlchemy.
Product development (actively in progress)
BankingGPT - AI assistant for complaint management in cooperative banking
- Security architecture at the core: no AI draft reaches the customer without human approval - the approval decision is in auditable code, not in the language model (monotonic: the model may escalate, never downgrade).
- Real agentic building blocks, each with its own boundary: the model chooses tools itself through an MCP server (read-only, allowlist, capped, fail-safe); sensitive cases are handed off via an open A2A protocol (JSON-RPC, Agent Card, message/send/tasks/get; client implemented by me) to a separate specialist agent (securities/law), which never lowers the review requirement (pinned by test).
- Evaluation-driven over ten analysis rounds; uncovered a security flaw through independent review and blind tests that nine automated runs had missed.
- Voice AI frontend, responding live: covered cases are answered in the conversation, sensitive ones escalate before generation; response latency < 7 s measured (local GPU STT/TTS).
Stack & production readiness: Python, pydantic-ai, FastAPI/Celery, PostgreSQL/pgvector, FastMCP, fasta2a, Docker; multi-tenant capable (physical vector isolation per tenant), PII encrypted, OWASP-LLM reviewed, 275 tests, CI/CD; vendor-portable (Ollama / EU Cloud Vertex).
After-Sales Assistant - agentic RAG/GraphRAG assistant on public OEM manuals (automotive after-sales)
- Genuinely agentic on LangGraph: ReAct agent with four tools and conversation memory - the model decides on its own whether to use the manual (RAG, Chroma), a knowledge graph (GraphRAG, Neo4j/Cypher - decodes warning lights), or a workshop/booking service.
- Human-in-the-Loop before the irreversible action: before every appointment booking, the graph pauses (interrupt) and gets the driver's explicit confirmation - the same approval-before-action discipline as in BankingGPT, in a different framework.
- Eval as CI gate: a three-part scorecard (RAGAS grounding + deterministic tool-routing accuracy + DeepEval safety: does the answer mention the warning first when there is a critical warning?) blocks the pipeline; provider-agnostic (OpenAI/Azure/Anthropic), FastAPI with token streaming.
Stack: Python, LangChain/LangGraph, Chroma, Neo4j, RAGAS/DeepEval, FastAPI, Docker.
Jovan J.
Last position:
CSV Manager, Technical Engineering at CureVac Printer GmbH
- Assist with the development of system requirements and specifications to ensure requirements are testable and 21 CFR Part 11 requirements are met
- Coach implementation teams in the proper execution of validation documents
- Evaluate proposed changes to validated computer systems and recommend level of validation activities required
- Coordinate audits of internal computer systems validation activities, protocols and procedures, and prepare responses
- Identify and qualify all computer systems impacting cGMP operations using a risk-based methodology
- Develop CFR Part 11 computer systems validation plans, qualification test protocols, traceability matrices, reports, IQ/OQ protocols and all deliverables within the scope of the validation plan
- Develop and maintain test plans, test scripts and user acceptance tests and manage their execution
- Act as CSV lead for all validation projects and execute or oversee validation plans and documents
- Perform project management activities for the CSV process within the scope of system projects
- Work with project manager to include validation activities in implementation timelines
- Manage internal CSV resources to facilitate completion of qualification activities
- Ensure initiation, preparation and closeout of all CSV-related deviations, discrepancies and change control documents
- Work closely with Validation Manager and QA Compliance to ensure appropriate validation of cGMP computer systems
- Conduct or facilitate validation and 21 CFR Part 11 training
Moez S.
Last position:
Data Engineer at Loschelder Rechtsanwälte Partnerschaftsgesellschaft mbB
- Designed a future-proof client database for marketing purposes
- Analyzed requirements, designed, and modeled an entity-relationship model
- Consolidated and optimized a client file from various data sources for targeted marketing campaigns
- Worked closely with marketing and IT in an agile environment to iteratively develop the solution
- Technologies and methods: MS Office (mainly Excel), MS Dynamics CRM, MS SharePoint
Sara A.
Last position:
Research Associate and Data Scientist at National Center of Robotics and Automation - Condition Monitoring Lab
- Developed ASR and TSR-based speech processing pipelines on AWS, enabling efficient feature extraction and scalable deployment for speech and text analytics.
- Built a Multimodal Speech Emotion Recognition system combining NLP and deep learning (audio + text), achieving 98% accuracy and supporting real-time, cloud-based inference.
- Designed and optimized end-to-end model training and evaluation workflows using AWS services (S3, EC2, Lambda) to ensure performance, reliability, and reproducibility.
- Created and deployed interactive, user-friendly dashboards for data visualization and insight generation, supporting research teams and management in data-driven decision-making.
Nooshin O.
Last position:
Senior Computational Biologist at Max-Planck-Institute for Molecular Genetics
- Conducting research at the interface of proteomics and artificial intelligence, focusing on the application of machine learning models (e.g., neural networks, clustering algorithms, and feature extraction) to analyze complex biological datasets.
- Developing and teaching AI-based analytical workflows for molecular and proteomic data, integrating tools such as Python (scikit-learn, TensorFlow, Pandas) for predictive modeling and data visualization.
- Collaborating with interdisciplinary teams to explore data-driven hypotheses in molecular genetics and enhance biological interpretation through AI-assisted pattern recognition.
- Implementing automated data processing pipelines to improve reproducibility and FAIR data management in high-throughput experiments.
Ahmad V.
Last position:
Data Scientist & AI Engineer at Exorbyte GmbH
- Lead engineer for the MatchMaker Toolbox (KNIME): Index Builder, Approximate Matcher, Character Mapper, license nodes
- Designed M|ARS (MatchMaker Agentic Retrieval System) — hybrid retrieval combining deterministic search + LLM tooling
- Developed internal RAG and search prototypes (MatchMaker + vector search + LLM)
Mohammed A.
Last position:
Data Scientist & Energy Consultant at Accenture GmbH
Alin-Florin R.
Last position:
Software Developer in the Smart Factory / Industry 4.0 field at Trebing & Himstedt
- Implementation and support of different customer project based on a given design specification and in compliance with the given solution architecture (requirement analysis, software conception, development, testing, Go-Live-Support, documentation)
- Extend of SAP standard components based to custom-specific requirements using different software tools (Java EE, SAP MII, XML, XSLT, REST, SOAP, SAP Netweaver)
- Implementation of SAP standard components from the manufacturing environment (SAP ME, SAP MII)
- Implementation and presentation of machine learning models for expos
- Presales support for the sales department
Discover over 15,000 top freelancers
Statistics of experts using Text Mining
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
2.1 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Project Management, Information Technology, Operations
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
25%
Certifications per freelancer
1
Most common languages
German, English, French
Speak two or more languages
100%
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 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 Text Mining
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.
Text Mining 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 (69%)
- Manufacturing (54%)
- Professional Services (54%)
- Automotive (38%)
- Banking and Finance (38%)
- Media and Entertainment (38%)
- Energy (31%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Text Mining Does
Text mining turns unstructured language into data that organisations can search, classify and analyse. It combines natural language processing, machine learning and linguistic methods to identify topics, entities, intent, sentiment and relationships across documents, messages, reviews or records. The result is structured insight that supports decisions and automation.
Common Applications
Text mining supports practical workflows across many industries and content types:
- Analyse customer feedback, support conversations and product reviews
- Classify documents for search, routing, compliance or knowledge management
- Detect themes, entities, opinions and emerging issues in large text collections
- Extract clauses, terms and risks from contracts, reports and correspondence
- Build recommendation, case-prioritisation and content-enrichment workflows
Tools and Ecosystem
Strong specialists work across Python libraries such as spaCy, NLTK, Gensim and scikit-learn, along with transformer models from the Hugging Face ecosystem. They may connect language pipelines to Elasticsearch, vector databases, SQL stores, notebooks and cloud services. Useful adjacent skills include data preparation, annotation, information retrieval, model evaluation, APIs and workflow orchestration. In Germany, multilingual processing often requires careful handling of German compounds, inflection, domain terminology and English-language content.
When Companies Need Help
Companies bring in freelance expertise when manual document review no longer scales, existing search returns weak results, or teams need reliable insight from feedback and records. A specialist can assess data quality, define a useful taxonomy and create a proof of concept before production work begins. External support is also valuable when an internal team needs a focused language pipeline without adding permanent capacity.
Delivery and Collaboration
A text mining project usually starts with representative documents, clear extraction goals and agreed evaluation criteria. Professionals clean and label samples, select rules or models, test edge cases and expose results through reports, search interfaces or APIs. Remote delivery works well when datasets, access controls and review routines are organised; on-site collaboration can help when domain experts hold sensitive records or complex business knowledge.
What Good Expertise Looks Like
Quality is more than choosing a language model. Strong professionals explain trade-offs between rules, classical machine learning, embeddings and generative approaches; prevent leakage between training and test data; and measure errors by category rather than relying on a single score. They document preprocessing, model versions, confidence thresholds and human review paths so the system remains understandable and maintainable after handover.
Frequently asked questions
Everything clients usually want to know about Text Mining, in one place.
Text Mining is used to extract structured information from unstructured language, such as topics, entities, sentiment, intent and relationships. Companies apply it to document classification, customer feedback, contract review, enterprise search and knowledge management.
Text Mining is often used as a practical umbrella for discovering patterns and extracting information from text. NLP describes the broader language-processing field, while text analytics usually emphasises reporting and interpretation; in real projects, the terms overlap and may involve the same tools.
A strong Text Mining specialist should understand data cleaning, annotation, linguistic analysis, information retrieval and model evaluation. Experience with Python, spaCy, Hugging Face, Elasticsearch, vector search, APIs and data governance is useful when the work must move beyond an experiment.
The right Text Mining experience depends on the task, data quality and operational risk rather than a fixed duration. A focused classification proof of concept may need a narrower background, while multilingual extraction, regulated records or production search require evidence of robust evaluation and deployment.
Text Mining work is often suitable for remote collaboration when secure data access, documentation and review sessions are available. German-language projects benefit from professionals who understand German terminology and can coordinate clearly with local domain experts, while sensitive records may require controlled on-site access.
In Text Mining, rules can be effective for stable formats, precise terminology and transparent decisions. Machine learning or transformer-based methods are usually more suitable when language varies widely, examples are available and the system must recognise patterns that are difficult to specify manually.
Assess Text Mining quality with a representative, human-reviewed test set and metrics suited to the task, such as precision, recall or extraction accuracy. Also check error analysis, performance across document types and languages, reproducible preprocessing, clear confidence handling and a practical review process.
Before starting Text Mining work, prepare sample documents, target outputs, known terminology, access constraints and examples of acceptable and unacceptable results. A clear business outcome and agreed evaluation method help the specialist choose the right pipeline and avoid building a technically interesting system that does not support daily operations.
The average hourly rate of freelancers in Germany who have used Text Mining in their recent projects is 96 €, which corresponds to a daily rate of about 771 € based on an 8-hour working day.
Of the freelancers in Germany who have used Text Mining in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers in Germany who have used Text Mining in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Germany who have used Text Mining in their recent projects are German (100%), English (100%), and French (15%).
The most common industries among freelancers in Germany who have used Text Mining in their recent projects are Information Technology (77%), Education (69%), and Manufacturing (54%).
The most common business areas among freelancers in Germany who have used Text Mining in their recent projects are Information Technology (85%), Product Development (85%), and Research and Development (85%).
Main locations of FRATCH Experts, who have recently used Text Mining
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 Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
