3. Data, Science & AI

Conducts statistical hypothesis tests including t-tests, chi-square, ANOVA, and p-value analysis for validation and A/B testing.

Creates reusable database routines using stored procedures and functions for complex queries and server-side logic.

Automates end-to-end ML pipelines including data processing, training, validation, and deployment using Airflow, Kubeflow, and Jenkins.

Measures relationships between variables using correlation coefficients, matrices, and association tests for relationship analysis and multicollinearity detection.

Performs survival analysis on time-to-event data using Kaplan-Meier and Cox proportional hazards models to estimate survival probabilities and compare groups.

Analyzes and optimizes database queries through execution plan review, index tuning, and slow query resolution to enhance performance.

Builds recommendation engines using collaborative filtering, content-based filtering, matrix factorization, and neural networks for personalized suggestions.

Performs exploratory data analysis using visualizations and summary statistics to uncover patterns and relationships in datasets for initial insights.

Identifies data anomalies using ML models (isolation forests, autoencoders) for fraud detection and quality monitoring.

Reduces feature dimensionality using PCA, t-SNE, and feature selection to improve visualization and computational efficiency.

Designs and implements efficient indexing strategies to optimize query performance in PostgreSQL and MySQL databases.

Builds collaborative and content-based recommendation engines to enhance product suggestions, personalization, and user engagement.

Classifies text sentiment using NLP and machine learning for customer feedback, brand monitoring, and opinion mining.

Designs normalized database schemas with relationships and constraints for PostgreSQL and MySQL, ensuring efficient data modeling and storage.

Analyzes cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy and treatment effect evaluation.

Builds NLP applications including text classification, named entity recognition, and sentiment analysis using transformer models and libraries like BERT and GPT.

Monitors production machine learning models for data drift, concept drift, and anomalies using Prometheus, Grafana, and MLflow.

Builds predictive models using linear, polynomial, and regularized regression for continuous prediction, trend forecasting, and relationship quantification.

Enables robust batch processing of large datasets using job queues, schedulers, and distributed workers for asynchronous data operations.

Optimizes database query performance by analyzing execution plans, implementing indexes, and refining queries to reduce response times and load.

Automates machine learning model hyperparameter optimization using grid search, random search, Bayesian optimization, and frameworks like Optuna and Hyperopt.

Analyzes user cohorts over time to calculate retention rates and identify behavioral patterns for customer lifecycle and retention insights.

Creates data visualizations with matplotlib and seaborn for exploratory analysis and stakeholder communication.

Analyzes user conversion funnels to identify drop-off points and provide data-driven insights for optimizing conversion rates.