Synthetic Data Experts in Germany
, matched in minutes by AIHire experts who create privacy-safe datasets, realistic edge cases and machine learning training data with tools such as Gretel, Mostly AI and SDV. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Germany, who have recently used Synthetic Data
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
Sven W.
Last position:
Simulation of Photometric-Stereo Setups at ID Engineering
- Role: Simulation Engineer
- Environment: Mechanical Engineering / Visual Inspection
- Goals & Implementation: Simulation of photometric-stereo setups to determine the best positions for cameras and light sources for each specific part.
- Business Value: Enabled a low-cost and scalable solution for determining part-specific hardware setups.
- Tech Stack: Python, Blender
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Martin R.
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)
Caner K.
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.
Ehsan A.
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.
Gabin N.
Last position:
Freelance Mathematics Expert for AI Model Training at Outlier AI and Mindrift AI
- Trained AI models to address specialized real-world problems
- Designed research oriented prompts grounded in applied mathematics, and developed rubrics criteria that consistently improve model reasoning and output quality
- Assessed the performance, accuracy, and reliability of advanced AI models
- Collaborated closely with cross-functional development teams to ensure AI models meet industry standards and provided actionable insights
Devakinand D.
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.
Kashyap K.
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.
Prasanna H.
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.
Thomas J.
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
Geraldine C.
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 R.
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 S.
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 A.
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.
Discover over 15,000 top freelancers
Statistics of experts using Synthetic Data
Aggregated from the professional profiles of matched freelancers.
Experience
10 years
Position duration
1.6 years
Positions per freelancer
7
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Information Technology, Research and Development, Quality Assurance
Bachelor's degree or higher
100%
Master's degree or higher
94%
Doctorate
39%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
95%
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 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Synthetic Data 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 (63%)
- Education (58%)
- Manufacturing (47%)
- Healthcare (37%)
- Banking and Finance (32%)
- Automotive (26%)
- Biotechnology (21%)
- Professional Services (21%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Synthetic Data Is
Synthetic data is artificially generated information that mirrors the statistical properties, structure and relationships of real data without reproducing individual records. It can represent tabular data, images, text, audio, sensor readings or transaction flows. Teams use data synthesis when real datasets are restricted, incomplete, expensive to obtain or poor at covering rare events.
What It Builds
Synthetic datasets support machine learning, software testing, analytics and simulation. They help teams train models, validate pipelines and test business rules before production data is available. Common applications include:
- Training computer vision, language and fraud detection models
- Generating rare cases for autonomous systems and medical research
- Testing APIs, data warehouses and customer journeys
- Simulating transactions, devices and operational events
Methods and Tooling
Professionals select methods based on the source data and the intended use. Common approaches include probabilistic models, generative adversarial networks, variational autoencoders and large language models. The ecosystem includes SDV, Gretel, Mostly AI, ydata-synthetic and custom Python workflows with pandas, PyTorch or TensorFlow.
Delivery Work
A typical engagement starts with data profiling, schema review and a definition of acceptable utility and privacy. Specialists then prepare source data, train or configure a generator, create datasets and compare them with the original through statistical and task-based tests. They also document limitations, reproducibility and safe access procedures.
When Companies Need Help
Companies bring in freelance expertise when a proof of concept must become a reliable data product, internal skills are limited or sensitive data cannot move freely between teams. In Germany, specialists may support automotive, manufacturing, insurance, healthcare and financial services projects while coordinating with local teams or working remotely. German and English communication may both matter for stakeholder workshops and documentation.
What Strong Experts Know
Strong professionals understand both generation and the business context behind the data. They can identify leakage, bias, memorization and weak coverage rather than treating realistic-looking records as proof of quality. Look for experience with privacy risk assessment, validation design, data pipelines, model evaluation and clear handover documentation. The best results preserve useful patterns without exposing real people or creating misleading conclusions.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Synthetic Data.
Companies use Synthetic Data to train machine learning models, test software and simulate events that are rare in real records. It is also useful when access to production data is delayed, restricted or too small for a reliable evaluation.
Synthetic Data is generated from learned patterns, while anonymized data is derived from real records with identifying details removed. It can reduce direct privacy exposure, but it still needs testing for memorization, bias, statistical distortion and suitability for the intended task.
A strong Synthetic Data specialist often combines statistics, machine learning, data engineering and privacy-aware governance. Useful related skills include Python, SQL, cloud pipelines, model evaluation, data quality testing and domain knowledge in areas such as finance, healthcare or manufacturing.
The right level depends on the work. A small dataset prototype may need a specialist who can select a method and validate outputs, while a production system needs deeper experience with monitoring, reproducibility, access controls and integration into existing data pipelines.
Yes. Synthetic Data work is often suitable for remote collaboration because profiling, generation and validation can run in controlled environments. On-site sessions may still help when teams need to agree on sensitive data rules, domain definitions or operational handover, and German or English may be required.
Assess Synthetic Data on several dimensions: similarity to the source, coverage of rare cases, performance on real downstream tasks and resistance to disclosure. A credible evaluation also checks bias, duplicates, memorization, missing relationships and whether the dataset supports the business decision it was created for.
SDV is an open-source ecosystem for generating synthetic tabular, relational and sequential data. It provides models and evaluation tools, but a specialist still needs to choose suitable constraints, prepare the source data and verify that the output is useful and safe.
No. Synthetic Data can be valuable when real examples are scarce or access is constrained, but it may reproduce gaps and assumptions from the source data. A specialist should compare synthetic and real-data performance and explain when generated records are unsuitable for production training or regulatory evidence.
The average hourly rate of freelancers in Germany who have used Synthetic Data in their recent projects is 69 €, which corresponds to a daily rate of about 550 € 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, 94% hold at least a Master's degree, and 39% hold a doctorate.
On average, freelancers in Germany who have used Synthetic Data in their recent projects have 10 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Germany who have used Synthetic Data in their recent projects are German (95%), English (95%), and French (11%).
The most common industries among freelancers in Germany who have used Synthetic Data in their recent projects are Information Technology (63%), Education (58%), and Manufacturing (47%).
The most common business areas among freelancers in Germany who have used Synthetic Data in their recent projects are Information Technology (95%), Research and Development (89%), and Product Development (79%).
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
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
