Data & BI Analyst with an M.Sc. in Statistics. I turn complex, multi-source data into validated metrics, semantic models, and decision-ready dashboards.
Currently open to Data Analytics, Business Intelligence, and analytics-focused Data Engineering roles.
Business Intelligence - Design and build Power BI reports, semantic models, and DAX measures. Import from PostgreSQL, MySQL, and file-based sources. Publish to Power BI Service with stakeholder access.
Analytics & Statistical Modeling - Regression, classification, clustering, hypothesis testing, and time-series analysis. Translate statistical results into business language.
Data Engineering Foundations - Python-based ETL, API ingestion, dimensional modeling, SQL analytics, and data-quality validation. Build pipelines that are documented and reproducible.
Applied AI Evaluation - Prompt and model comparison, LLM output evaluation, taxonomy validation, and structured-tag extraction from unstructured text.
| Category | Tools |
|---|---|
| Languages | SQL, Python, R |
| Data manipulation | pandas, NumPy |
| Databases | PostgreSQL, MySQL |
| BI & visualization | Power BI, DAX, Power Query, semantic models |
| Statistics & ML | Regression, classification, clustering, hypothesis testing, model evaluation |
| Workflow | Git, GitHub, Jupyter Notebook, R Markdown, Excel |
Axion Ray - Data Analyst Analytical metrics, SQL-based logic, and quality validation for AI-assisted data workflows. Evaluating LLM outputs, comparing prompt and model configurations, and validating structured tags extracted from unstructured text.
Dozee - Data Analytics Intern Built and validated Power BI reports, created semantic models, imported data from PostgreSQL, MySQL, Google Sheets, and Excel, and published reports to Power BI Service for controlled stakeholder access.
End-to-end BI and data-engineering project using public synthetic FHIR data. Covers Python extraction and transformation, PostgreSQL dimensional modeling, SQL analytics with CTEs and window functions, data-quality checks, and a five-page Power BI dashboard with a city-level drillthrough page.
Python · PostgreSQL · SQL · ETL · Dimensional Modeling · Power BI · DAX
Customer segmentation applying statistical and machine-learning analysis to identify meaningful groups within credit-customer data, with business-oriented interpretation of the resulting segments.
Customer Analytics · Clustering · Statistical Analysis · Python · Data Visualization
Research-oriented statistical modeling project examining factors associated with menstrual-hygiene choices. Emphasis on interpretable analysis, responsible communication, and evidence-based conclusions.
Statistical Modeling · Research · Hypothesis Testing · R · Interpretability
- Business question first. I start with what decision the analysis supports, not with which tool to use.
- Reproducible by default. Analysis is scripted, documented, and rerunnable. Results trace back to source.
- Validation before reporting. Metrics are checked before they reach a dashboard or a stakeholder.
- Honest about limitations. Assumptions, data-quality issues, and uncertainty are documented alongside results, not hidden underneath them.
- Communication matters. A finding only a statistician can interpret is half-finished.
- LinkedIn - linkedin.com/in/rutuja-kadam-01ab77269
- Email - kadam.rutuja143@gmail.com
- GitHub - github.com/rutu6103
Based in India. Open to on-site and hybrid roles in Bengaluru, Pune, Mumbai, and Navi Mumbai.