Matplotlib Experts in Germany

• Task Engineering: Evaluating Coding Agents.
• Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
• Prompting & Translation: finding failure points where AI does not work, in German.
• Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
• Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
• Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
• Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.

• Developing a model-based AI where natural language is interpreted as formal relations.
• Natural language terms are not considered rigid but fluid and can be negotiated in a context so meaning resolves by iteratively specifying.
• Develops all kinds of model solutions.
• Backed by natural language and data annotation.
• Requirements to code and other solutions.

• 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

Responsibility for commercial finance in Central Europe (DACH and Poland), reporting to the EMEA Commercial Finance Director. Monthly financial reporting, forecasting, and variance analysis, evaluation of promotions and special campaigns, management of planning processes including budgeting, as well as preparation of QBR materials up to CFO level. Took over functional leadership in the finance team after the mandate holder was unavailable.

• Title: Enhancing Syntactic Awareness in Transformer Language Models for Hindi Dependency Parsing
• Investigated syntactic knowledge captured by transformer language models (RoBERTa, XLM-RoBERTa) for Hindi dependency parsing, a morphologically rich and low-resource language.
• Developed structure-aware model variants (Struct_Roberta_hi, Struct_XLMR) by integrating a CNN-based parser network between transformer layers, inspired by the StructFormer architecture.
• Conducted extensive error analysis including label-wise, distance-based, direction-based, sentence length-based, and LVC/Non-LVC evaluations.
• Evaluated models on downstream NLP tasks (NER, POS tagging) using the IndicXTREME benchmark.

• Managed and maintained daily sales and transaction data, ensuring data accuracy and integrity for operational reporting and analysis.
• Analyzed customer purchasing patterns to support inventory planning and improve product availability.

• Automotive SPICE®: all assessments fully achieved
• Agile transformation: V-model → SAFe successfully implemented
• Series release: on-time, quality-assured software delivery for key Volkswagen Group models (including ECE homologation)
• Stakeholder management: internal & external
• Process optimization: implemented a continuous improvement process (CIP) with a tracking system

• Designed and implemented a Python-based ETL pipeline with the Dagster framework to transform raw energy data from heterogeneous sources using InfluxDB and visualizations in Grafana
• Defined time-based and dependency-based jobs
• Deployed to managed Kubernetes clusters using Helm
• Integrated InfluxDB Cloud
• Prepared data for use in Grafana, including cleaning, normalization, and time-based resampling in Python
• Developed dashboards and visualizations in Grafana
• Developed unit tests with mocking using pytest
• Set up a CI/CD pipeline in GitLab

Technologies: Python, Dagster, InfluxDB, Grafana, pandas, pytest, REST, CI/CD, GitLab, Container, Kubernetes, Helm, Docker, Cloud

• Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
• Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
• Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment

• Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
• Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
• Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
• Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
• Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow

Self-directed exploration of agentic AI development methods, taken from idea to a working, publicly usable application

• Built an interactive planning tool for modelling retirement withdrawals and tax strategy using an agentic AI (vibe coding) development approach – demonstrating self-directed investigation of new AI-assisted development methods
• Delivered live, tax-aware spending projections and adjustable user inputs; shipped as a free, install-free browser application built in Python, with attention to usability for non-technical users

• Topic: Object Detection and Semantic Segmentation for (AUV) Systems using Transformer-Based Vision Models and Sensor Fusion.
• Designed and implemented an end-to-end multi-sensor fusion perception pipeline (Camera, LiDAR, IMU) in ROS
• Developed CNN-based Machine Learning model (YOLOv8) and Transformer-based vision models for real-time object detection
• Processed and clustered 3D LiDAR point clouds using DBSCAN, RANSAC, and voxel grid filtering to enable robust object localisation in noisy environments.
• Designed Bayesian Network models (GeNle) for probabilistic reasoning and sensor-level decision fusion under uncertainty.
• Applied Kalman filtering for sensor state estimation, temporal alignment, and smooth object tracking, reducing false positives in safety-critical scenarios.
• Evaluated system performance under realistic driving dynamics, improving tracking stability and overall perception robustness.
• Built deep learning pipelines for training, validation, and performance evaluation of perception models using sensor data.

• Partner with commercial, sales, and medical affairs teams to translate business questions into structured analyses and interactive Power BI dashboards, enabling data-driven decisions in a regulated pharma environment.
• Design and maintain Power BI dashboards that integrate data from Veeva CRM, SharePoint and Databricks, providing real-time visibility into sales trends, territory performance, and commercial KPIs across multiple markets.
• Query and join multiple tables in Databricks using SQL to build clean, analysis-ready datasets, applying transformations such as filtering, aggregation, and window functions to prepare data for reporting.
• Implement Power Automate flows to automate data refresh processes and trigger alerts for KPI thresholds, improving the timeliness and reliability of commercial analytics reporting.

• Requirement-based test case design for automated parking maneuvers.
• Implementing and running test cases.
• Integration within the existing AVP (Automated Valet Parking) SW framework.