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Developed and optimized data integration pipelines for ESG data collection and processing, incorporating external and internal data sources, APIs, and data providers to streamline ESG data collection (Python, DynamoDB, FastAPI, Kafka, Docker).

Guided a cross-functional software engineering team, fostering collaboration, mentoring, and skills development.

Working for an outsource company on a plethora of projects using different technology stacks and development processes:

• Successfully introduced an internal attribution control system, eliminating the need for third-party integrations

• Implemented REST microservices for SMS campaign functionality using Tornado and Python, enhancing the platform's capabilities and ensuring seamless integration with external SMS providers

• Played a pivotal role in improving the stability and performance of the suppression lists functionality by optimizing ETL processes

• Managed a cross-functional team of software developers and quality engineers, overseeing multiple projects centered around nonlinear mixed-effect modeling.

• Responsible for requirements analysis, prioritization, creating SRSs and SDSs, technical design, implementation, and testing

• Developed various parts of the system: clinical trials simulation, model optimization, RShiny interfaces, and many others

• Participated in conferences in collaboration with PK/PD department

• Explored recent research articles on applications of ML for classification and face recognition tasks.

• Created document recognition and data extraction (OCR) system utilizing transfer learning and achieved more than 98% accuracy in recognition

• Implemented POCs for face comparison and fraud detection during face recognition

Researched current world achievements in the restriction maps acquisition process and known approaches for assembling. Refactored and parallelized existing code, suggested improvements to the algorithm (C++). Implemented alignment and merging routines for a new algorithm for de-novo assembling of a human genome from a set of noisy optical maps. Achieved significant (more than x10) improvements in performance while keeping the same results quality (C++, Python)

Built an ontology in Blazegraph based on numerous data sources (Orphanet, OMIM, GHR, GO, HPO, Clinical Trials, UniProt etc.) and implemented search algorithms to find hidden relationships and disease similarities based on the corresponding terms (C#, SPARQL)

Developed parts of Sentient Suite applications which integrate heterogenous data from semantic (Virtuoso, Allegro Graph, Oracle RDF) and relational data sources and allow search and visualization of it (C#, RDF, OWL, SPARQL, SQL)

Analyzed biomarkers of PML disease using genomic and transcriptional datasets. Found correlations between drug usage and condition using SVM for classification and prediction (R, Bioconductor, Python)