skills
The working toolkit, honestly framed. Everything listed here is something I have genuinely used, and the last section is what I'm learning right now, listed openly because trajectory matters.
Python & data manipulation
Comfortable with: my daily working environment for analysis and modeling.
- Python
- pandas
- NumPy
- Matplotlib
- Plotly
- Jupyter
Demonstrated in Customer Churn Prediction
SQL & databases
Working knowledge: window functions, CTEs, cohort analysis, and data-quality auditing.
- SQL
- SQLite
- window functions
- CTEs
- cohort analysis
Demonstrated in Revenue & Retention Analytics
Machine learning
Working knowledge: supervised learning with a baseline-first, evaluation-focused workflow.
- scikit-learn
- logistic regression & gradient boosting
- cross-validation & temporal validation
- leakage prevention
- precision@k, ROC-AUC, log-loss, Brier
- calibration & error analysis
Demonstrated in Customer Churn Prediction
Statistics & experimentation
Working knowledge: hypothesis testing and knowing when a result is noise. Backed by a mathematics degree.
- hypothesis testing (chi-square, t-test, z-test)
- A/B test design & evaluation
- effect sizes (Cramér's V, Cohen's d)
- regression
Demonstrated in Revenue & Retention Analytics
Time series & forecasting
Working knowledge: walk-forward backtesting and leakage-safe feature design.
- walk-forward backtesting
- lag & rolling features
- forecast metrics (MAE, WAPE)
Demonstrated in Demand Forecasting
Data visualization & communication
Comfortable with: charts that state a takeaway, dashboards built around decisions, and write-ups a non-technical reader can follow.
- Matplotlib
- Plotly
- Streamlit
- KPI design
- written analysis
Demonstrated in Retail Operations Dashboard
Workflow & tooling
Comfortable with: reproducible, version-controlled, tested project work.
- Git & GitHub
- pytest
- virtual environments
- reproducible pipelines
Currently learning
In progress: listed openly because trajectory matters as much as the current toolkit.
- PostgreSQL & BigQuery
- deep-learning fundamentals
- dbt & data pipelines
Why no skill bars?
Percentage bars and star ratings look precise but measure nothing. Prose framing like “comfortable with”, “working knowledge”, and “currently learning” is what I’d actually say in an interview, so it’s what the page says too.