Position: Data Scientist
Category: Data Science
English level: upper
Years of experience: 4

With my assistance, the company automated its work process and reduced the time of processing customer applications from 10 min to 30 sec with an increase in general profit.
I've designed and implemented the ML and MLOps systems from scratch. They are being used in commercialized production applications right now. Looking forward to working on interesting projects that might include ML and possible MLOps integrations!
Throughout my Data Science career, I've worked on several different projects that included the realization of programming skills as well as leadership skills. The most impactful ones:
1. Designing a ML approach for the system of analyzing bank transactions: building a data labeling app, training a ML model, deploying and monitoring the best version.
2. Building ML pipeline for credit risk scoring and converting it to the MLOps pipeline using GCP and KubeFlow on top of the Kubernetes cluster. Automated continuous training and monitoring of versions of the models.
Main stack of technologies: Python, pandas, TensorFlow, Keras, PyTorch, XGBoost, GCP, KubeFlow, Docker, Power BI
Looking forward to increasing my skill in MLOps by building and designing ML pipelines for different commercial projects.