Tesla | Autopilot & FSD Division — Los Angeles, CA
Part of Tesla's Autopilot team, focused on AI safety for Full Self-Driving. Background in data analytics and BI — 4+ years building consolidated reporting, financial audits, and applied ML projects.
Now studying Computer Science at Santa Monica College, targeting UC Berkeley. Building toward ML/DL engineering and robotics.
LinkedIn · nurbol.sultanov@gmail.com · Tableau Public
Languages: Python (pandas, NumPy, scikit-learn, SciPy, statsmodels, Plotly), SQL (PostgreSQL, T-SQL)
ML & Statistics: Logistic Regression, Random Forest, Gradient Boosting, LightGBM, A/B testing, hypothesis testing, feature engineering
Time Series: Prophet, SARIMA/ARIMA, walk-forward cross-validation, anomaly detection
AI/LLM: Anthropic Claude API, RAG, sentence-transformers, embeddings, semantic retrieval, prompt engineering
BI & Visualization: Tableau, Power BI (DAX, Power Query), Excel (advanced)
Analysis: RFM segmentation, cohort retention, churn, demand forecasting, KPI dashboards
Tools: Git, Jupyter, VS Code, Streamlit
Multi-Entity Retail Analytics Consolidated sales, margin, and inventory-shrink analysis across a 3-entity, 12-location retail group, unifying two POS systems into one reporting layer. Per-entity and network roll-ups, category margin, and an audit-lens shrink model. Stack: Python, pandas, Plotly, Streamlit, Jupyter
Demand Forecasting + Anomaly Detection Time-series forecasting on Rossmann Store Sales (1M rows, 1,115 stores). LightGBM reached 8.20% MAPE, roughly 2x better than Prophet and SARIMA. Anomaly detection on residuals. Stack: Prophet, SARIMA, LightGBM, statsmodels, Plotly
Live Tableau Dashboards
- Credit Risk Portfolio — 5K loans, $91.7M, Grade F 30x default rate
- Payment Fraud Detection — 50K transactions, ATM 2x POS fraud rate
- Insurance Claims Analysis — 15K claims, $78.4M, Mental Health 22% denial rate
- 2026–present — AI Safety Data Collection, Tesla (Autopilot/FSD Division)
- 2022–2026 — Data Analyst, Damir Bary Inc.
- 2020–2021 — Freelance Data Analyst
- 2020 — Contract Data Analyst, Volkovgeology JSC (Kazatomprom subsidiary)
- 2015–2019 — Internal Auditor, NAC Kazatomprom (LSE-listed uranium producer)
- Santa Monica College — Computer Science (transferring to UC Berkeley, target Fall 2028)
- Kaggle — Pandas, Advanced SQL (2026)
- Salesforce — Agentblazer Champion 2026, Agentforce Builder, 25+ Trailhead modules
- Google Data Analytics — Foundations, Ask Questions (2026)
All projects
| Project | Tools | Description |
|---|---|---|
| Multi-Entity Retail Analytics | Python, pandas, Plotly, Streamlit | 3-entity / 12-location retail roll-up across two POS systems, margin + shrink analysis |
| Demand Forecasting + Anomaly Detection | Prophet, SARIMA, LightGBM, Plotly | Rossmann 1M rows, walk-forward CV, LightGBM 8.20% MAPE |
| Credit Default Prediction | Python, scikit-learn | Loan default model, LR vs RF vs GB with feature engineering and threshold tuning |
| A/B Testing Case Study | Python, SciPy, statsmodels | Checkout conversion A/B test with power analysis and significance testing |
| SQL Case Studies | PostgreSQL | 5 advanced SQL patterns: cohort retention, Top N per group, running totals, gap-and-island, LTV |
| Credit Risk Portfolio Analysis | Python, SQL, Tableau | Default rate segmentation by grade, income, vintage cohort |
| Payment Fraud Detection | Python, SQL, Tableau | Fraud pattern analysis by channel, merchant, time-of-day, geography |
| Insurance Claims Analysis | Python, SQL, Tableau | Claims cost and denial rate by plan, provider, denial reason |
| E-commerce Customer Segmentation | Python, SQL | RFM, churn, and cohort retention for a French fashion retailer |
| Drilling OPEX Analysis | Python, SQL, Power BI | Operational cost analysis for uranium drilling across 12 deposits |
| Supply Chain Delay Analysis | Python, SQL, Power BI | Shipment delay root cause across ports in Southeast and East Asia |
| Marketing Campaign Dashboard | SQL, Python, Power BI | ROAS, CTR, conversion rate across channels and campaign types |
| Retail Sales Analysis | SQL, Python | Revenue and customer behavior analysis of POS transactions |
| MedTransport BI Analytics | Python, pandas, Jupyter | Synthetic NEMT company: KPIs, cohort retention, channel performance, unit economics |
| Social Media Engagement Analysis | SQL, Python | 10K posts across 5 platforms: engagement drivers, content type, campaign effectiveness |