Position: Data Scientist | ML Engineer
Category: Data Science
English level: upper
Years of experience: 3

- Achieved 92% accuracy for logo object detection problem using Yolor.
- Developed model with 90% accuracy for sentiment analysis problem using TF-IDF and Logistic regression.
- Achieved 93% accuracy for car color detection problem using yolo, wrapped it into Fast API and docker.
- Made speaker identification model with 95% accuracy. 
- Increased accuracy of speaker diarisation model by 30%
- Set up Ambari HDP cluster with connected PowerBi dashboard.
- Made GCP infrastructure that increased neural network training speed and workflow 10 times.
Worked since Sep 2020 to Feb 2023 at Go Wombat Team at Data Scientist position.

During this period I worked at Silverbullet company's project at outstaff basis. It was aimed to build contextual targeting, brand safety and insights platform. I contributed to sentiment analysis and face detection models. Built logo detection model using state of the art approaches. Also worked on dataset creation and annotation via AWS MTurk. The last field of work was creating understandable and informative dashboard for existing models demonstration.
My second project was to build a car color detection model that could be easily ported to C++ and run on the road cameras.

Worked since Apr 2019 to Jan 2020 at AM-BITC LLC at Platform Engineer position.

During this period of work I worked on several projects. The first and main project was "Speaker Identification by Voice". Working with a colleague we achieved 95% accuracy. Also at this project I made back- and frontend for our Flask application. Next project that I done was cloud based analytics in PowerBi made in Microsoft Azure. After that I studied and completed Cisco CCNA course. Also it was time, when I deployed Tableau, Tibco and PowerBi servers.

Worked since Feb to Mar 2019 at Technorely Inc. at Junior ML Engineer position.

During this period I worked on Speech Recognition project. My task was to solve "speaker diarisation" problem. I took two most perspective existing models and improved it. First was written on tensorflow, second on PyTorch. During improving I used clustering methods (Spectral, K-means and others), wrote metric for evaluating, tried several combinations of architecture and dived into sound processing domain scope.

Worked since Aug to Nov 2018 at uData.tech at Technical Specialist position.

During this period I worked in DS team on three projects. On Financial project we were trying to answer the question "Disposition effect, good or bad?". I worked with pandas, matplotlib, seaborn Python libraries. On medicine project we were predicting the next disease of the patient via recommendation system, decision rules, RNN and LSTM models. I worked on 3-node Hadoop Cluster on PySpark visualizing and cleaning data. My last project was at agro sector. The main purpose was to build BI system and make price forecast. I scraped data, made database and Tableau dashboard.
Seeking interesting projects that need my skills.