Data Science & Analytics

Perform automated exploratory data analysis including statistical summaries, correlation matrices, distribution plots, and outlier detection.

Build and evaluate predictive models by automating feature selection, algorithm comparison, and hyperparameter tuning workflows.

Process unstructured text with entity extraction, topic modeling, sentiment analysis, and text classification in configurable pipelines.

Create interactive business intelligence dashboards with charts, KPIs, and drill-down capabilities from raw data sources.

Automatically detect and resolve data quality issues including missing values, duplicates, format inconsistencies, and encoding errors.

Detect unusual patterns and outliers in time-series and tabular data using statistical and machine learning approaches.

Generate and evaluate feature candidates from raw data using transformations, aggregations, and domain-specific feature creation strategies.

Evaluate A/B test results with statistical significance testing, confidence intervals, and sample size recommendations.

Build and deploy time-series forecasting models for demand, revenue, and metric prediction with confidence intervals and seasonal decomposition.

Convert natural language questions into optimized SQL queries with schema-aware context and result interpretation.

Design and configure ML model deployment pipelines with versioning, monitoring, A/B testing, and automated retraining capabilities.

Assess data governance maturity, identify PII exposure risks, and recommend data cataloging and lineage improvements.