Senior Product & Marketing Analyst with 12+ years turning data into revenue across
gaming, fintech, HR-tech, and staffing.
Expanding senior analytics experience into applied ML, experimentation, analytics engineering, and GenAI.
Based in Charlotte, NC | Green Card | Open to U.S. opportunities
I build SQL-first product and marketing analytics: funnels, cohorts, LTV/ROAS, A/B testing, executive KPI systems, and stakeholder-ready recommendations. My background includes analytics leadership at G5 Entertainment and Sberbank HR-tech, plus current U.S. staffing analytics work.
Over the last two years, I have expanded into applied ML and GenAI where they improve decision quality: calibrated churn probabilities, cost-aware thresholds, source-grounded RAG, explainability, testing, and deployable Python apps.
Open to roles: Senior Data Analyst | Senior Product Analyst | Data Scientist | Analytics Engineer | Applied AI / GenAI Analytics roles
| Project | What it shows | Verified result | Demo | Repo |
|---|---|---|---|---|
| Telecom Customer Churn Prediction | Calibrated churn scoring, thresholding, SHAP, lift analysis, Streamlit deployment | Test ROC-AUC 0.8397; PR-AUC 0.6551; recall 0.9278 at threshold 0.12; top-decile lift 2.828 | Demo | Repo |
| A/B Test Analyzer | Product experimentation, hypothesis testing, Bayesian cross-checks, rollout guardrails | Conversion increased from 42.0% to 66.0% (+24.0 pp, p=0.008026); time-on-page increased by 1.691 minutes (p=0.000139) | Demo | Repo |
| Medical Q&A RAG Assistant | Source-grounded RAG, citations, safety fallback, inference-only GenAI deployment | RAGAS across 20 questions: answer relevancy 0.9644; context precision 0.9278; context recall 0.6067; faithfulness 0.5735 | Demo | Repo |
| SuperKart Revenue Prediction | Retail revenue regression, prediction intervals, SHAP, Streamlit/FastAPI deployment | RMSE $281.04; MAPE 4.23%; 73.7% RMSE reduction vs naive baseline; 85.7% interval coverage | Demo | Repo |
| Hotel Booking Cancellation Risk Scoring | Hospitality risk scoring, cost-aware thresholding, SHAP, revenue-protection framing | Test ROC-AUC 0.9172; PR-AUC 0.8385; recall 0.9167 at threshold 0.31; top 30% by risk captures 77% of cancellations | Demo | Repo |
Recommended recruiter order: Telecom Churn, A/B Test Analyzer, Medical RAG first. SuperKart and Hotel are strong secondary projects with live demos.
These projects support the analytics-engineering and senior analyst side of my profile:
| Project area | Signal | Repo |
|---|---|---|
| A/B testing in SQL | Experiment design, metric definition, statistical thinking | Repo |
| Retention cohorts | Cohort logic, repeatable retention views, product analytics | Repo |
| LTV / ROAS funnels | Growth analytics, marketing efficiency, acquisition quality | Repo |
| Feature engineering for ML | SQL-to-ML data preparation and reusable feature logic | Repo |
| ETL / Redshift-style pipeline | Data modeling, warehouse thinking, reproducibility | Repo |
Analytics and BI
Statistics and Experimentation
Databases and Warehouses
Machine Learning
GenAI and LLM
Deployment and MLOps
- The decision layer matters more than the model score. Calibrated probabilities, cost-aware thresholds, and a clear action path turn analysis into leverage.
- Experiment results need guardrails. A statistically significant conversion lift still needs retention, revenue, performance, and novelty-effect checks before broad rollout.
- Cohorts beat snapshots. Retention curves, LTV by acquisition month, and funnel-stage cohorts reveal stories that aggregate KPIs hide.
- Production hygiene matters. If a model is meant to influence decisions, it needs reproducible code, saved artifacts, tests, documented limitations, and a usable interface.
- Email: aloskutov.ds@gmail.com
- LinkedIn: linkedin.com/in/andreyloskutov
- Hugging Face: huggingface.co/andreyloskutov
- Website: Website
- Location: Charlotte, NC - open to U.S. remote, hybrid, or on-site roles
For recruiters: project map by role
| Target role | Best projects to review first |
|---|---|
| Senior Data Analyst | A/B Test Analyzer; Telecom Customer Churn; Hotel Booking Cancellation |
| Data Scientist | Telecom Customer Churn; SuperKart Revenue Prediction; Hotel Booking Cancellation |
| Product Analyst | A/B Test Analyzer; Telecom Customer Churn; Hotel Booking Cancellation |
| Analytics Engineer | SuperKart Revenue Prediction; Telecom Customer Churn; SQL analytics projects |
| ML Engineer | Telecom Customer Churn; SuperKart Revenue Prediction; Medical Q&A RAG Assistant |
| Applied AI Engineer | Medical Q&A RAG Assistant; Telecom Customer Churn; SuperKart Revenue Prediction |
Strongest signal: 12+ years of senior analytics experience plus the ability to ship practical ML and GenAI workflows when the business problem calls for them.