Position: Machine Learning Researcher
Category: Data Science
Experience: 2.5 years | English: upper

1. Developed a method for the embedding of the non-differentiable computation blocks in the training pass of the NNs without breaking the gradient flow. The method has boosted the project and was employed by other engineers.

2. Developed a novel approach to the ABSA which involved combining several solutions that were non-conventional/not intended for this task but allowed for wider and more flexible analysis. That helped our clients better understand their users' feedback by gaining a more nuanced look due to the power of DL.

3. Performed semi-automated prompt engineering for LLMs like GPT-J with the purpose of creating highly personalizable promotional messages.

4. Successfully led a junior team member towards professional growth and accommodating the specifics of the NLP R&D process.
Data scientist, RBC Group, 10/2020 - 12/2020
Machine Learning Engineer, Adoriasoft, 02/2021 - 10/2021
Machine Learning Engineer, FreySoft, 11/2021 - 12/2021
NLP Researcher, Team Lead, Sandsiv+, 01/2022 - 04/2023

I now have almost 3 years of experience in building ML/DL pipelines with 2.5 years of commercial experience in the field including team-leading experience.

My current job is aimed at developing various textual NLP solutions for the VoC platform that allows our clients to better understand their customers' feedback.

My previous position involved developing personalized marketing solutions using various NLP methods. Before that, I was working on DL-based steganography (undetectable watermarking) with different content modalities, primarily employing Computer Vision techniques.

I have experience applying Transformers architectures (GPT-J/Neo/2, BERT-like models, Temporal Fusion Transformer, ViT, etc.) and GANs (regular and WGANs) to solving custom problems. In my work, I leveraged the prompting of large pre-trained models and the development of custom neural nets with specific training procedures like off-graph usage of non-differentiable computation blocks or Triplet loss for Siamese nets.

I have a strong interest in NLP, willing to dig deeper into the area of controllable disintegrated text generation. My latest approaches to this involved entailment detection, semantic similarity, prompt engineering, and ABSA.

I'm familiar with popular frequency transforms (DCT, DFT, DWT) and have experience applying these for steganographic purposes.

Reinforcement learning basics (DQ learning).

I have publications in professional scientific journals, in particular on the topic of ML/DS, e.g. I've proposed a way of dealing with the Peter principle in hierarchical organizations via using machine learning.

I have a general understanding of DeFi and have implemented interpretable ML-based credit scoring systems, including via SHAP values analysis.

English - B2/C1

AWS EC2/S3, GCP, Vertex AI experience

Basic SQL knowledge (PostgresSQL)

Basic knowledge of HTML5/CSS3

MS Office, Adobe Photoshop, MS SharePoint Designer
I'm looking for a job that would allow me to broaden my ML/DL skills and realize my interests. 
What I don't want is ML for the sake of ML and applying AI simply as a hyped instrument.