Dimitar Gochev

• Develop and maintain the infrastructure that enables the training, deployment, and maintenance of machine learning models in a production environment, including providing on-demand access to these models through HTTP APIs.
• Develop a domain-specific language for aggregated operations on tabular data and lead the migration of existing computational libraries to this specific language.
• Develop data ingestion pipelines that provide foundations for model enhancements and evaluation.

Technologies:

• Python, CI/CD, Docker, Kubernetes, AWS, MLflow, Luigi

• As an analytics engineer at Experian, I worked on behavioral and application credit risk modeling. I was involved in all stages of a model's development: data preparation, feature derivation and selection, model training, validation, documentation, monitoring, and fine-tuning.
• I had also taken part in the development of a pipeline designed to provide data preparation capabilities for any kind of model development.

Programming languages and frameworks:

• Python (pandas, scikit-learn, SciPy, Luigi, some PySpark experience)

Models:

• Linear and logistic regressions, decision trees, XGBoost

• I worked on extensions for a machine learning framework that provided advanced analytics to a business analytics platform (Qlik).
• Programming Languages: Python (pandas, scikit-learn)

• I was part of a research team that developed chatbots designed to answer questions regarding the SAP Cloud Platform documentation. In addition, I developed cloud-native applications to enhance the chatbots' performance.
• Programming languages: Python and Java