Особисті дані приховані

Last updated in June 2025

Kostiantyn Krykunov
 Kyiv  [відкрити контакти](див. вище в блоці «контактна інформація»)  [відкрити контакти](див. вище в блоці «контактна інформація»)
 www.tumblr.com/raincameracover
 https://github.com/Kkrykunov?tab=repositories
 0000-0001-8301-6021

Professional Summary
A seasoned Data Analyst transitioning into a Python Engineer role, combining med-
ical expertise with advanced Python and SQL skills to enhance healthcare solutions.
Proven impact at WHO with automation tools improving data accuracy by 30% and
reducing processing time by 40%.

Experience
July 2024 – June 2025 Data Analyst Consultant, World Health Organization (WHO) – Ukraine Country
Office – Ukraine
• Normalized 16,000+ PHC facility names using Python NLP and fuzzy matching.
• Developed an API that accelerated processing speed, allowing the normalization
of 16,000 names sooner than planned and improving overall data management
efficiency.
• Built automated validation pipeline ensuring 98% data accuracy.
• Developed a facility registry for the entire Kharkiv region, improving data acces-
sibility and advancing regional healthcare planning and resource allocation.
• Created a comprehensive analytical research framework for OTC kits evaluation,
significantly improving data analysis accuracy and influencing product enhance-
ment strategies.
• Developed a methodology for measuring distribution output and impact, leading
to improved tracking and assessment of OTC kits’ effectiveness.
• Analyzed accessibility patterns across conflict-affected populations, optimizing
resource allocation and improving support delivery efficiency.
• Developed a detailed initial findings report for stakeholder review, which gener-
ated valuable data-driven insights that enhanced strategic planning, improved
cross-departmental collaboration, and supported key business decisions.
• Processed f110 forms for EMS data from Odesa and Mykolaiv oblasts, ensuring data
accuracy and completeness for subsequent analysis.
• Engineered a highly efficient database structure for emergency response data,
boosting retrieval speed and accuracy, which led to faster decision-making and
improved emergency responses.
• Conducted initial data analysis and validation, uncovering critical trends that
ensured data integrity and facilitated strategic decision-making.
• Prepared and organized data for future dashboard development, resulting in
enhanced accuracy and efficiency in data visualization and reporting.
• Enhanced Python automation tools for facility assessments, achieving 30% greater
data accuracy and a 40% reduction in processing time.
• Built RESTful APIs for real-time data access, enhancing data retrieval efficiency and
supporting timely decision-making.
• Created reproducible analysis pipelines using Git/GitHub, ensuring consistent data
analysis and facilitating collaboration among team members.
• Delivered critical PHC analysis 1.5 months ahead of schedule through innovative
API solution, enabling faster humanitarian response.

Kostiantyn Krykunov - Page 1 of 3
 • Standardized data collection across humanitarian partners, resulting in 25% faster
response times and enhanced collaboration in humanitarian efforts.
• Developed an API solution using AWS and TensorFlow, completing critical PHC
analysis 1.5 months early to speed up humanitarian response efforts.
Feb 2014 – July 2024 Data Analyst, Medical Research Projects – Kyiv, Ukraine
• Contributed to statistical analysis for 7 PhD research projects using advanced
modeling techniques.
• Enhanced and automated data pipelines for processing medical records, boosting
efficiency by 30% and increasing data accuracy by 25%.
• Developed and implemented predictive models for surgical outcomes that im-
proved decision-making with an 85% accuracy rate, enhancing overall surgical
success.
• Developed comprehensive reproducible research frameworks with Python, R, and
Git, enhancing research consistency and efficiency in data analysis projects.
• Designed REDCap databases for multi-site clinical trials, enabling seamless data
access and management, which improved research coordination and data integrity
across all sites.
• Developed machine learning algorithms for patient risk stratification that in-
creased predictive accuracy by 25%, leading to more timely interventions in patient
care.
• Published analytical methodologies using SPSS and GitHub, enhancing data quality
and speeding up research by 20%.
July 2009 – June 2024 Pediatric Cardiac Anesthesiologist, Ukrainian Children Cardiac Centre
• 14 years of clinical experience in pediatric cardiac surgery providing domain
expertise for healthcare analytics projects.

Education
May 2025 – May 2027 Neoversity, MSc in Computer Science
• Coursework: AI & Machine Learning Track
Sept 2023 – June 2025 National University of Kyiv-Mohyla Academy, MPH in Public Health – Kyiv
Sept 2004 – Jan 2010 Bogomolets National Medical University, MD in Medicine
• Transformed from medical doctor to data analyst at 40+, showcasing adaptability

Courses
Sept 2024 – Dec 2024 Kyiv-Mohyla Academy, in Digital Medicine course in Project Management – Kyiv
Jan 2023 – June 2024 Projector Institute, in Python Programming
Feb 2023 – July 2023 Private tutor, in Linear Algebra
Jan 2022 – May 2025 Private tutor, in Python Advanced
Sept 2024 – Dec 2024 Kyiv-Mohyla Academy MPH program, in Big Data Analysis – Kyiv
Jan 2025 – June 2025 Kyiv-Mohyla Academy MPH program, in Blockchain Tools – Kyiv
Jan 2020 – June 2020 Coursera, in Game Theory

Achievements
• Transformed from medical doctor to data analyst at 40+, showcasing adaptability
• Reduced analysis time by 90% at WHO
• Participated in ETHKyiv 2025 Hackathon
• Published papers bridging medicine and data science

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• Built scalable data processing systems

Skills
Programming: Python (Pandas, NumPy, Flask), SQL, R, JavaScript, Full Stack Devel-
opment, Solidity, TensorFlow, SPSS, Statistics, Rest APIs, System Design
Analytics: Time Series (ARIMA/VAR), Machine Learning, Statistical Modeling, A/B
Testing
Visualization: Tableau, Power BI, Matplotlib, Plotly, D3.js, plotnine, Data Pipelines
Big Data: Apache Spark, Hadoop (completed full big data course), ETL Pipelines, Data
Warehousing
Tools: Git/GitHub, Docker, AWS, Jupyter, VS Code, claude code, gemini, R Program-
ming, GPU programming, Linux Administration, Risk Management, Budget Manage-
ment

Interests
• Large and medium frame photography

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