Synthetic Data Experts in Germany

Enterprise Agile Planning Platform

Key achievements:

• Introduced and promoted shift-left testing practices.
• Established and maintained QA metrics to track release quality and support data-driven decision-making.
• Reviewed Selenium-based automated tests, identifying coverage gaps and prioritizing improvements with the automation team.

Responsibilities:

• Managed QA activities throughout the release lifecycle, coordinating testing efforts and ensuring release readiness.
• Mentored and onboarded 10 QA engineers and fostered knowledge sharing.
• Conducted technical interviews and participated in candidate evaluation.
• Collaborated with stakeholders to improve quality practices.

• 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

• 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.

• 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)

• 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.

• 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.

• 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.

• 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

• 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.

• 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

• 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.

• 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

• 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.