Position: Data Scientist / Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 5

- Lead a team of 3 Data Scientists for 4 months, overseeing end-to-end ML product development.
- Together with 2 other developers took over and maintained in production a Machine Learning Framework developed and supported by a team of 20 in San Francisco.
- Got accepted to Cambridge for PhD in Civil Engineering with an industrial researched project for my company (didn't start the program bc of the changes at the company).
- TOEFL 30/30 for reading and listening with only 1 hour preparation for the test.
I have been working as a Data Scientist specializing in ML/DL for the past 4 years. In the past two years I focused on Computer Vision applications to ultrasonic sensor images from Oil&Gas pipeline inspections, which involved both designing and implementing the models, as well as deployment of the dockerised applications. My models have delivered 17% automation of manual pipeline analysis. 

Before that I have been responsible for end-to-end development of ML applications for supply chain, logistics and mechanical engineering use cases covering clustering, prediction, forecasting and linear optimization. 

During this time I have developed a solid expertise on classical ML as well as Deep Learning and Computer Vision methodology, productionizing models under rigorous review process, while upholding to high coding standards with unit test coverage, using Git and Jenkins for CICD and docker for containerizing ML applications. 

I have also worked on side projects in medical imaging, like instance segmentation of chest X-rays using U-net network architectures, and image super resolution of PET CT Scans using Variational Autoencoders. 

As a Data Scientist at GE, much of my work has involves investigating existing academic literature on ML and NN architectures in order to adapt the results and fine-tune the implementation to the use-case at hand. A good practice of mine for staying up to date with recent advancements

Tech Stack: 
- Python, Tensorflow/Keras, Scikit-learn, numpy, pandas, H2O, R;
- Linux, AWS (EC2, S3), Docker, Jenkins;
- Tableau, R Shiny, ggplot, seaborn, matplotlib.
I am interested in working on Machine Learning / Deep Learning applications, preferably in Computer Vision, but NLP and time series forecasting is also of interest. It's important that the work is closer to the main product of the company. REMOTE ONLY