Position: Applied Scientist
Category: Data Science
English level: fluent
Years of experience: 9


I am an Applied Scientist with around 9 years of experience in prototyping/building various NLP and multi-modal models. At my current job and previous positions my tasks included:
• Building/experimenting with Vision Language models (ALBEF) of different sizes to introduce usage of visual
elements into the slot carryover model used within home assistant system
• Leading a team of 5 scientists in building/experimenting with multilingual distilled models with various techniques (logit matching, tinyBERT, teacher logit annealing, etc.) to improve upon prod models (results published at EMNLP and KDD). Distilled models are used at various building blocks of the home assistant system
responsible for ASR, NLU, conversation and etc.
• Implementation of a framework for distilling student models from teacher models of transformer architecture on various tasks in a multi-GPU setup
• Prototyping/experimenting with machine translation for automatic language and feature bootstrapping in home assistant system (results published at NAACL)

Finding trigger events in news
• Classifying emotions in customer emails
• Knowledge extraction from handbooks
• Classification of texts into market segments and sentiment classes
• User-intent classification for automating FAQs
• Confidentiality level classification of internal and external documents
• Named Entity Recognition and relation extraction for different use cases (persons, organizations, conductor-
performer-title relations from radio broadcast descriptions, investment targets from annual reports, etc.)
• Prototyping near duplicate detection in real time with streams (Using Elasticsearch as storage)
• Building content based ranking algorithm for concepts within texts that was used for ad selection (250%
improvement in click through rate)
• Implementation of plugins for Elasticsearch and query components for Solr, generic scraping tool for forums
• Development of custom libraries and refactoring of legacy code, making visualizations with Kibana

Languages/frameworks/tech stack: Python (PyTorch, sklearn, gensim, pytorch tensorflow, keras, spacy, grpc, flask, scrapy,
numpy, Deepspeed, transformers (HuggingFace), datasets(HuggingFace)), kubernetes (EKS), AWS, Java and Groovy (opennlp, deeplearning4j, lucene, crawler4j, norconex, etc.), Scala (spark, kafka), Elasticsearch/Kibana, Solr, AWS