Data Scientist currently finishing a Masters in Data Science at Freie Universität Berlin. With 5+ years of professional experience in related fields – my expertise lies in developing and deploying advanced Machine Learni…
Data Scientist currently finishing a Masters in Data Science at Freie Universität Berlin. With 5+ years of professional experience in related fields – my expertise lies in developing and deploying advanced Machine Learning and Deep Learning models for ASR, Computer Vision, Natural Language Processing (NLP), and Predictive Analysis. Experienced with end-to-end model development, API integration, and cloud deployment (AWS, Docker, Kubernetes), I care about creating ML/Neural Networks solutions for real-world impact. I thrive in fast-paced environments and am open to full-time opportunities and relocation.
Developed advanced Voice Assistant models for cars, focusing on Natural Language Understanding (NLU) and real-time inference, improving inference efficiency and response latency in production pipelines.
Automated C++ code generation for NLU using custom domain templates and integrating LLMs (TensorFlow, PyTorch), cutting manual efforts by more than 90%.
Developed advanced Voice Assistant models for cars, focusing on Natural Language Understanding (NLU) and real-time inference, improving inference efficiency and response latency in production pipelines.
Automated C++ code generation for NLU using custom domain templates and integrating LLMs (TensorFlow, PyTorch), cutting manual efforts by more than 90%.
Worked on the DFG-funded project “Fairness and Efficiency in Vehicle Routing Problems” at the Dahlem Centre for Machine Learning and Robotics.
Designed novel algorithms with feasibility constraints and prepared layered data visualizations using Python (Matplotlib, Seaborn) and HTML/CSS for enhanced interpretability.
Worked on the DFG-funded project “Fairness and Efficiency in Vehicle Routing Problems” at the Dahlem Centre for Machine Learning and Robotics.
Designed novel algorithms with feasibility constraints and prepared layered data visualizations using Python (Matplotlib, Seaborn) and HTML/CSS for enhanced interpretability.
Led end-to-end projects in Computer Vision, Demand Forecasting, Synthetic Data Generation and Predictive Analytics.
Created and deployed CV models for custom Object Detection, Text Extraction and Image Enhancement.
Used Docker and AWS services to create API endpoints for ML models with SAM and Lambda, and leveraged SageMaker for scalable model training.
Collaborated cross-functionally using Agile methodologies and delivered product demos to clients.
Awarded “ZinStar” and recognized as the AI lead of the winning team in a company-wide Hackathon.
Led end-to-end projects in Computer Vision, Demand Forecasting, Synthetic Data Generation and Predictive Analytics.
Created and deployed CV models for custom Object Detection, Text Extraction and Image Enhancement.
Used Docker and AWS services to create API endpoints for ML models with SAM and Lambda, and leveraged SageMaker for scalable model training.
Collaborated cross-functionally using Agile methodologies and delivered product demos to clients.
Awarded “ZinStar” and recognized as the AI lead of the winning team in a company-wide Hackathon.
Architected backend solutions (using Oracle PL/SQL) for an enterprise-level insurance application.
Implemented new features, led production support operations by providing code-level bug fixes for critical issues, and mentored associates on best practices in coding and process management.
Recognized with multiple awards including ‘Star of the Month’ and ‘Best Team’ for exceptional performance.
Architected backend solutions (using Oracle PL/SQL) for an enterprise-level insurance application.
Implemented new features, led production support operations by providing code-level bug fixes for critical issues, and mentored associates on best practices in coding and process management.
Recognized with multiple awards including ‘Star of the Month’ and ‘Best Team’ for exceptional performance.
Performed data cleaning and feature engineering on 1k+ survey responses, built predictive models, and presented data-driven insights for a Menstrual Health Management program.
Integrated Apache cTAKES to enhance the company's healthcare product, focusing on improved data extraction and NLP.
Training on data annotation tools, object detection methods, and image augmentation techniques, essential for Computer Vision projects.
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