Synthetic Data Experts in Germany
in minutes from over 15,000 CVs with the power of AI.Hire experts who design synthetic datasets for testing, privacy-safe analytics, and model training. They work with data generation pipelines, validation rules, and domain-specific edge cases. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used Synthetic Data
Ehsan Amin
Last position:
Clinical Data Scientist at Freelance
- Conduct data management and statistical analysis for clinical studies on behalf of CROs.
- Guest lecturer at Ivancity University, Paris, specializing in data anonymization techniques and statistical disclosure control.
- Provide scientific and medical writing services for pharmaceutical companies.
- Perform optical mapping data analysis and develop software tools with a focus on algorithm optimization and technical support.
Devakinand Dama
Last position:
Master's Thesis: Analyzing Prompt Engineering for Data Extraction from Unstructured Data at Technical Institute of Rosenheim
- Applied advanced machine learning techniques by developing a multi-strategy prompting framework (zero-shot, few-shot, CoT, instruction tuning) to extract structured data from complex financial and medical datasets, significantly enhancing model reliability and achieving an 18% improvement in F1-score through rigorous evaluation using advanced metrics (ROUGE-L, METEOR, Cosine Similarity).
- Designed scalable structured-output workflows and built automated monitoring pipelines (spaCy, ClearML) for continuous performance tracking, simulating real-world MLOps principles.
- Refined prompt strategies iteratively based on meticulous error analysis to ensure robust, production-ready performance.
Thomas Jahn
Last position:
Test Manager at Hamburger Energienetze GmbH
- Method: none
- Environment: ERP, S4/Hana, Confluence, Jira, Xray, SAP Solution Manager, Tosca
- Created the test concept for the migration from ERP to S/4HANA
- Created manual test cases and test plans for the acceptance test
- Coordinated tests between the business testers
- Test automation with Tosca
- manual testing
Prasanna Hegde
Last position:
Master's Thesis Student at Robotics Research Lab, TU Kaiserslautern
- Benchmarked datasets, collected real-world off-road data (~9000 images) and generated simulation datasets using Unreal Engine.
- Developed Gen-AI image segmentation in Unreal Engine, reducing time from 1-2 days to 5-10 minutes.
- Built Generative AI data enhancement pipeline. Achieved an improvement in synthetic data by +49% mIoU.
- Tech: Python, PyTorch, C++, Git, LangChain, Gen-AI, Linux, W&B, OpenCV, Labelme.
Geraldine Castillo
Last position:
Solution Engineer (Data & ML Integration) at Amadeus Data Processing GmbH
- Designed ML-ready data integration workflows between on-premise systems and cloud platforms (Snowflake, AWS Redshift, Azure), enabling scalable feature engineering and model deployment
- Implemented automated ML pipeline deployment using Python, SQL, and CI/CD tools, reducing model deployment time by 60%
- Developed data transformation logic for master data synchronization across ERP and analytics systems, ensuring data quality for predictive models
- Collaborated with cross-functional teams to translate business requirements into mathematical specifications for ML solutions
Sushant Rao
Last position:
Senior Data Scientist at INES Analytics GmbH
- Led the implementation of ETL pipelines across multiple products with diverse data and reporting requirements, incorporating data cleaning, validation, and preparation layers.
- Managed a rotating team of 2–3 data scientists (total 6) to develop and deploy multiple data science projects across company products, managing project timelines and deliverables.
- Collaborated with Backend, DevOps and Frontend teams to integrate data science pipelines into production, ensuring seamless delivery on schedule.
- Developed a probabilistic synthetic data generation system to produce statistically faithful data twins, containerized using Docker for reproducible deployment; validated through alpha testing with 5 development partners for privacy-preserving analytics and reporting.
- Designed and built a prescriptive analytics module with scenario simulation to support data-driven decision-making.
Athul Sivan
Last position:
Data Scientist at Science to Data Science – Deutsche Welle
- Built a GPT-based synthetic data pipeline that reduced acquisition cost and turnaround time by more than half.
- Modeled audience behavior across underrepresented groups using prompt workflows and statistical validation.
- Evaluated data realism with clustering, regression, and divergence analysis.
- Delivered reproducible Python workflows to automate experimentation in an Agile environment.
- Translated analytical results into clear insights for content and strategy teams.
- Technologies and skills: Python, Generative AI, GPT, Machine Learning, exploratory data analysis, Agile, GitHub, cloud computing, hallucination analysis.
Vasco Almeida
Last position:
AI Research Intern – Generative AI at BMW AG
- Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
- Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
- Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.
Martin Ratajczak
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
Robin Steinkühler
Last position:
Consultant, Data Science & Engineering at valantic Digital Finance GmbH
- Bridged business and engineering for enterprise finance clients, designing data products and cloud pipelines in Python, SQL Server, SAP Datasphere, and Tagetik
- Conceived, built, and containerised a Python/FastAPI universal connector that syncs SAP S/4HANA and other SQL/NoSQL sources to Tagetik, deployed on Google Cloud Run and Microsoft Azure, cutting a critical 90-minute data load to approximately 80 seconds (65× faster)
- Architected a medallion-layer SQL Server warehouse ingesting approximately 500 GB/day from 11 ERP instances, automating daily refreshes (full load under 6 minutes) and freeing 20–30 finance staff from days of manual data consolidation
- Led cross-functional workshops to design enterprise EPM target architecture for a leading Southeast-Asian telecom (CAPEX, OPEX, revenue), translating requirements into data-model specifications and integration blueprints now being built by the client’s implementation team
- Delivered selected projects including a consolidated data & reporting warehouse for a global manufacturer (10 k+ employees), NFI reporting for an international management & technology consultancy, and CAPEX/OPEX planning for a Southeast-Asian telecom (20 k+ employees)
Caner Karaoğlu
Last position:
Synthetic Medical Dataset (MedGym) at MedTank
- Generated synthetic datasets for CXR, mammography, and distal radius fracture detection using GANs and diffusion, creating >50k synthetic images for benchmarking.
- Ensured GDPR-compliant workflows and reproducibility, enabling dataset adoption for internal validation and academic collaboration.
- Project highlighted in MedTank’s internal R&D showcase as a flagship synthetic data initiative.
Aniruddha Pal
Last position:
AI Software Developer at Sentics GmbH
- Developed a Python-based synthetic data generation pipeline in Blender to simulate complex human-forklift interactions for robotic perception and AI model training.
- Designed and modeled 3D industrial digital twins to support depth estimation, stereo vision, and safety analysis workflows.
- Collected and processed LiDAR, laser, and photogrammetry point clouds to generate accurate 3D maps for environment reconstruction and ground-truth data creation.
- Developed and deployed YOLOv8-based pose estimation and depth perception algorithms using PyTorch and OpenCV, optimized for GPU clusters and NVIDIA Jetson platforms.
- Integrated and validated AI modules in ROS-based robotic environments, ensuring real-time performance and interoperability.
Muskan Verma
Last position:
AI Engineer at Sagas IT Analytics
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search; cut research time by 30%.
- Designed custom retrieval workflows with LlamaIndex, building a ReAct-style agent for dynamic chunking; improved query accuracy by 18%.
- Researched and optimized embedding strategies, reducing retrieval cost/query by 15%.
- Developed RAG evaluation frameworks using RAGAS and Langsmith with custom datasets; improved coverage by 40%.
- Fine-tuned LLMs (LLaMA 2 on Vertex AI with custom inference containers, dynamic batching, and quantization); reduced inference latency by 25%.
- Integrated AI agents in LangGraph with short-term & long-term memory (Mem0); increased task completion rate by 20%.
- Created schema-aware synthetic data generators; fine-tuned downstream models achieving +12% F1 score.
Kashyap Khunt
Last position:
Master’s Thesis - Synthetic Data Generation for Quality Inspection at Schaeffler Technologies AG
- Developed a synthetic data generation framework using 3D simulation (NVIDIA Omniverse) and Generative AI (Stable Diffusion) to model and augment industrial surface defects.
- Trained and evaluated Computer Vision models (YOLO, DETR), achieving 94% detection accuracy on real-world samples and demonstrating successful simulation-to-reality transfer.
- Applied domain adaptation to improve simulation-to-reality transfer, enabling scalable Industrial AI for automated quality inspection and reducing manufacturing downtime.
Hyosang Kim
Last position:
Student Assistant at Mbition | A Mercedes-Benz Company
- Enhanced in-car voice assistant responsiveness by optimizing engineering solutions for audio data processing.
- Improved dataset quality and model robustness by generating synthetic data with Hugging Face sources.
- Increased contextual relevance of voice assistant outputs through optimized AI prompt engineering.
- Reduced transcription errors by debugging inconsistencies between audio inputs and transcripts.
- Boosted team productivity and software delivery speed by automating workflows and actively driving Agile updates.
Discover over 15,000 top freelancers
Statistics of experts using Synthetic Data
Aggregated from the professional profiles of matched freelancers.
Experience
9 years
Position duration
1.5 years
Positions per freelancer
6
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Education, Information Technology, Manufacturing
Certification focus areas
Information Technology, Research and Development, Quality Assurance
Bachelor's degree or higher
100%
Master's degree or higher
93%
Doctorate
21%
Certifications per freelancer
1
Most common languages
German, English, Hindi
Speak two or more languages
93%
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 Synthetic Data
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
Synthetic data is data that is created to mirror the shape, patterns, and edge cases of real data without exposing the original records. Teams use it when they need safer sharing, better test coverage, or more training material than they can use from live systems.
Where it fits
- Software testing and QA with realistic records
- Analytics where sensitive fields must stay hidden
- AI and machine learning training sets
- Demo environments and product walkthroughs
- Edge-case data for fraud, risk, or workflow checks
Tools and methods
Work usually spans Python, SQL, and data pipelines, plus libraries and services for tabular, text, image, or time-series generation. Strong specialists know how to preserve distributions, correlations, constraints, and rare cases, not just create random fake rows.
When companies bring in experts
Teams often need freelance help when they must replace masked data with safer synthetic alternatives, build repeatable generation jobs, or test a new product with realistic records. In Germany, this is common in regulated industries, enterprise software, and cross-border projects where data access is tightly controlled.
What strong specialists deliver
A strong professional can define the target schema, choose the right generation approach, and prove that the output is useful for the task. They also check privacy risk, document assumptions, and align the data with downstream consumers such as analysts, testers, and model teams.
What to look for
- Clear grasp of privacy and data quality trade-offs
- Experience with tabular and unstructured synthetic datasets
- Ability to test realism, coverage, and constraint handling
- Comfort working with product, data, and security stakeholders
- Practical delivery, not just proof-of-concept examples
Frequently asked questions
The facts hiring teams ask for most often when it comes to Synthetic Data.
Synthetic data is used to create realistic records for testing, analytics, training, and demos without exposing sensitive source data. It helps teams work with edge cases, fill gaps in rare scenarios, and share data more safely across systems and partners.
Synthetic Data is generated, while anonymized or masked data still starts from real records. That difference matters when you need stronger privacy control or want data that is safe to distribute more widely. Masking can be simpler, but it often keeps hidden traces of the original dataset.
A strong Synthetic Data specialist usually knows SQL, Python, data modeling, and validation methods. Depending on the project, they may also need experience with privacy review, machine learning workflows, or test automation. The best people can talk to both data and product teams clearly.
The right level depends on the use case, but Synthetic Data work is rarely just about creating fake rows. For a simple demo or test dataset, a focused expert may be enough. For privacy-sensitive or model-driven projects, you want someone who can design the generation method and verify the result.
Yes, most Synthetic Data work can be delivered remotely if the expert gets access to the schema, business rules, and sample outputs they need. In Germany, remote collaboration is common for distributed product and data teams. On-site time can still help when access constraints or stakeholder workshops are important.
Ask how the freelancer will preserve structure, constraints, and rare cases in Synthetic Data. Also ask how they will measure usefulness for testing or training, and how they will handle privacy risk. A good answer should mention validation, not only generation.
High-quality Synthetic Data looks useful for the task, not just believable at first glance. It should follow the schema, respect business rules, and cover the cases your team needs. It should also come with documentation so others know how it was built and where it should be used.
People often use Synthetic Data alongside terms like artificial data or fake data, but the intended quality is different. In serious projects, the goal is to generate data that follows real patterns and rules. That makes it suitable for testing, analytics, and model work, not just placeholders.
The average hourly rate of freelancers in Germany who have used Synthetic Data in their recent projects is 65 €, which corresponds to a daily rate of about 523 € based on an 8-hour working day.
Of the freelancers in Germany who have used Synthetic Data in their recent projects, 100% hold at least a Bachelor's degree, 93% hold at least a Master's degree, and 21% hold a doctorate.
On average, freelancers in Germany who have used Synthetic Data in their recent projects have 9 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Germany who have used Synthetic Data in their recent projects are German (93%), English (93%), and Hindi (13%).
The most common industries among freelancers in Germany who have used Synthetic Data in their recent projects are Education (53%), Information Technology (53%), and Manufacturing (40%).
The most common business areas among freelancers in Germany who have used Synthetic Data in their recent projects are Information Technology (93%), Research and Development (87%), and Product Development (73%).
Main locations of FRATCH Experts, who have recently used Synthetic Data
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
