Text Mining Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Text Mining
Chintan Padaliya
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 calculation with 150,000+ validated data records
Smart API workflows for real-time carbon footprint calculations in ERP and ESG systems
ML algorithms to predict emission hotspots and optimize product design
Automated data validation pipelines with NLP for quality assurance of CO₂e datasets
Led a 15-person cross-functional team to develop 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% team velocity increase)
Product-market fit for AI features through A/B testing and analytics (60% higher adoption rate)
Philipp Grunert
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
Moez Seyedan
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
Dieter Ratz
Last position:
Driver analyses at Genactis GmbH
- Calculation of attribute importance based on driver analyses
- Interpretation, reporting, and consulting
Valery Khamenya
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
Niko Karajannis
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.
Sara Ali
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.
Jovan Jelic
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
Nooshin Omranian
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.
Marco Pennacchiotti
Last position:
Head of Data Science and Data Engineering at Entrix
- Established and leading multi-year research roadmap
- Developed and implementing hiring plan for science and data
- Spearheading data engineering efforts in the company
- Led the team to deploy a new trading algorithm, increasing assets’ revenue of 18%
Ahmad Varasteh
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 Abdallatif
Last position:
Data Scientist & Energy Consultant at Accenture GmbH
Alin-Florin Roman
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
16 years
Position duration
2 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 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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it covers
Text mining turns emails, tickets, reviews, reports, and contracts into structured insight. It is used for search, tagging, topic discovery, sentiment analysis, and entity extraction. Teams also call it text analytics or, in broader projects, part of NLP and information extraction.
Common deliverables
- Document classification and topic grouping
- Named entity and keyword extraction
- Sentiment and intent analysis
- Search enrichment and semantic indexing
- Text pipelines for analytics and reporting
Core toolset
Strong specialists work with Python, spaCy, NLTK, scikit-learn, transformer models, and search stacks such as Elasticsearch or OpenSearch. They also know how to clean noisy text, handle multiple languages, and evaluate precision, recall, and relevance with care.
When to bring in help
Companies usually need freelance support when text data is messy, volume is high, or internal teams need a clear path from raw content to usable features. In Germany, this often comes up in customer support, legal review, publishing, insurance, and industrial knowledge search, where German language handling matters.
What good specialists do
They do more than run a model. They define label sets, prepare training data, inspect errors, and tune rules and models together so results stay useful in production. They also write clean handover notes so teams can maintain the setup after the project.
Signs you need expertise
- Search results miss the right documents
- Manual tagging takes too long
- Text sources come in many formats or languages
- You need reliable extraction from contracts or cases
- Existing NLP results are hard to trust
Frequently asked questions
Everything clients usually want to know about Text Mining, in one place.
Text mining is used to turn large amounts of unstructured text into structured information. Common uses include document classification, topic discovery, sentiment analysis, search enrichment, and extracting names, dates, or product terms from text. It is often part of broader NLP work, but the focus is usually on usable output for search, reporting, or automation.
Text mining is a practical approach to finding patterns and signals in text, while NLP is the wider field that includes parsing, generation, and language understanding. Text analytics is often used as a business-facing term for similar work, especially when the goal is dashboards or operational insight. In projects, the terms overlap, so the exact scope matters more than the label.
A strong text mining specialist can clean text, design labels, choose methods that fit the use case, and measure whether results are actually useful. They should understand both rule-based methods and machine learning, including modern transformer-based approaches when they add value. Good work also includes clear documentation and realistic limits.
A text mining project often needs Python, data cleaning, SQL, search engineering, and some understanding of machine learning. Depending on the use case, experience with Elasticsearch, OpenSearch, or vector search can also help. For regulated or knowledge-heavy environments, domain understanding is often as important as the tools.
Not every text mining project needs a deeply senior profile, but the harder the data and the stricter the quality target, the more experience matters. Simple classification or tagging can be handled by a practical specialist, while extraction from legal, medical, or multilingual content needs stronger judgment. The key is matching the scope to the complexity of the text.
Text mining for German needs careful handling of compounds, inflection, and domain terms. That affects tokenization, search, entity extraction, and classification quality. For companies in Germany, it helps to work with specialists who have real experience with German text rather than only English-centric models.
Most text mining work can be done remotely because the core tasks are data review, model work, and evaluation. On-site collaboration can help at the start if the text sources are sensitive, scattered across teams, or tied to internal workflows. Many companies use a mixed setup: remote delivery with short working sessions on-site when needed.
Look for a text mining specialist who asks about your data, your labels, and how results will be used. Good signs include clear evaluation methods, sample error analysis, and a plan for handling noisy or ambiguous text. If the person can explain trade-offs in plain language and connect them to your business goal, that is usually a strong signal.
The average hourly rate of freelancers in Germany who have used Text Mining in their recent projects is 91 €, which corresponds to a daily rate of about 728 € 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 16 years of professional experience, with a single engagement typically lasting around 2 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 (62%), 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
