Data Engineer

The candidate is a machine learning engineer with 7+ years of experience in developing machine learning algorithms and solutions. She has worked with various types of neural networks, machine learning algorithms, and statistical methods, and has experience in using programming languages such as C++, Python, and MATLAB. The candidate is familiar with Google Cloud Platform and machine learning libraries such as Keras, Caffe, and TensorFlow. She also has experience in communication and interaction with medical specialists. The candidate holds a specialist degree in applied mathematics and informatics from the National Research Nuclear University MEPhI. She has worked as a machine learning engineer and a mathematical software developer, where she developed software for data analysis systems and implemented various algorithms. The candidate is skilled in software development, mathematical algorithms, and has proficiency in Python, C++, and various machine learning libraries. She is fluent in Russian, has intermediate proficiency in English and Slovak, and basic knowledge of German. In her free time, The candidate enjoys swimming, hiking, roller skating, playing chess, and reading fiction, scientific, and professional literature.

Jan. 2018 - Present: Machine Learning Engineer, Company A

• Developed a model for classifying the state of bipolar affective disorder patients based on actigraphy data using Google Cloud Platform, Python, and machine learning libraries.
• Implemented a convolutional neural network model for playground segmentation in video streams, specifically identifying and tracking players in basketball and soccer games.
• Created a model for classifying lung nodules as benign or malignant using convolutional neural networks.
• Conducted research on multimodal semantic segmentation for cars and people using lidar and camera data, evaluating existing algorithms and providing guidance to junior team members.
• Worked as a C++ software engineer on an automotive telematics platform, participating in design, development, testing, and debugging of microservices.

Aug. 2013 - Jan. 2017: Mathematical Software Developer, Company B

• Developed mathematical software for the Polyanalyst data analysis system, focusing on clustering, classification, and regression analysis using various neural network algorithms and statistical methods.
• Implemented algorithms for data preprocessing, feature selection, and overfitting avoidance using C++ and libraries such as STL and Boost.
• Utilized Polyanalyst statistical nodes and library functions for data exploration and parallelization of computations.

Sept. 2011 - Aug. 2013: Research Assistant, Company C

• Processed and analyzed patients' monitoring data using statistical methods and neural networks at a research institute specializing in urgent traumatic surgery and trauma.
• Utilized MATLAB and self-written algorithms for analyzing time series data and medical actions.