Research / Data Analyst | Systems & Signal Processing | Product & BI Analytics

I work at the intersection of research analytics, data analysis and systems thinking: I break down complex domains, build reproducible Python/SQL workflows, analyze time-series data and turn fragmented information into structured analytical conclusions.

My focus is not only to calculate metrics or build dashboards, but to understand how a system works, what data it generates, what measurement limitations exist and what conclusions can be made without overstating the result.

• analysis of technical and scientific sources;
• structuring of complex subject domains;
• preparation of concise analytical conclusions, limitations and recommendations;
• comparison of approaches, trade-offs and potential risks.

• SQL / PostgreSQL;
• analytical data marts and metric logic;
• cohort analysis, retention analysis, funnel analysis and A/B testing;
• data quality checks and reproducible reporting.

• time-series and event-data analysis;
• detection of periodic structure in noisy observations;
• phase reconstruction and signal interpretation;
• work with data limitations, noise and incomplete measurements.

Repository: https://github.com/kva99kva-eng/pulsar

Research-style project based on Vela pulsar photon event data.

The project demonstrates a full analytical workflow:

• loading and inspecting FITS photon event data;
• preprocessing photon arrival-time observations;
• detecting a dominant pulsation period;
• building folded pulse profiles;
• demonstrating simplified phase-shift logic behind X-ray pulsar navigation.

Why it matters: this project shows how I approach physical and engineering data: not as a generic dataset, but as an indirect observation of a real system with noise, constraints and interpretation limits.

Repository: https://github.com/kva99kva-eng/sleepmind-sql-product-analytics

PostgreSQL product analytics project for a synthetic sleep-tracking product.

Focus areas:

• data model design;
• cohort retention;
• onboarding funnel;
• A/B test evaluation;
• churn-risk segmentation;
• product recommendations based on SQL analysis.

Why it matters: this project demonstrates BI / Data Platform skills: analytical tables, product metrics, segmentation logic and structured business conclusions.

Repository: https://github.com/kva99kva-eng/sleepmind-ai

Product analytics + ML project for a sleep-tech MVP.

Focus areas:

• synthetic user-day dataset;
• product metrics;
• ML baseline for sleep quality prediction;
• threshold analysis;
• rule-based AI sleep coach logic.

Why it matters: this project connects product analytics, interpretable ML evaluation and product decision-making.

Repository: https://github.com/kva99kva-eng/eeg-cognitive-load-detection

EEG cognitive-load classification project with signal features, ML baselines and validation strategy.

Focus areas:

• spectral features;
• subject-independent validation;
• leakage analysis;
• baseline ML models;
• Streamlit demo.

Repository: https://github.com/kva99kva-eng/Sleep-Staging-Fragmentation-Detection

Sleep-EDF based project focused on sleep staging, sleep fragmentation and sleep quality metrics.

Focus areas:

• sleep-stage data processing;
• fragmentation metrics;
• sleep-quality feature extraction;
• reproducible notebook-based analysis.

Repository: https://github.com/kva99kva-eng/psychiatric-brain-connectivity-analysis

Exploratory fMRI functional-connectivity analysis across diagnostic groups.

Focus areas:

• functional-connectivity matrices;
• group-level comparisons;
• statistical testing;
• careful interpretation of limitations.

• Python
• SQL / PostgreSQL
• pandas / NumPy
• SciPy
• Matplotlib
• scikit-learn
• Jupyter Notebook

• data cleaning and EDA;
• time-series analysis;
• signal-processing basics;
• period detection and phase analysis;
• cohort / retention / funnel analysis;
• A/B testing;
• ML evaluation;
• leakage-aware validation;
• statistical interpretation of limitations.

• technical documentation;
• research summaries;
• analytical reports;
• README / project documentation;
• translation of technical findings into clear conclusions for both technical and non-technical audiences.

I am currently focused on roles where analytics is close to research, engineering and systems thinking:

• Research Analyst;
• Data / BI Analyst;
• Systems Analyst;
• Аnalytics roles in space-tech, digital health or complex technical products.

I am especially interested in projects where data reflects the behavior of a real technical, physical or product system — and where analysis requires both computation and careful interpretation.