Work Experience

Responsibilities & Achievements:

• Designed, trained, and validated predictive models (classification, regression, clustering) in Python using Scikit-learn, applying cross-validation and documenting assumptions for reproducibility.
• Designed and developed end-to-end ETL pipelines and data models using SQL and Python, enabling structured data ingestion, transformation, and reporting while optimizing AWS cloud storage and compute resources for performance and cost efficiency.
• Developed and deployed NLP pipelines (sentiment analysis, topic modeling, clustering) leveraging LLMs and ML frameworks.
• Orchestrated end-to-end data and ML workflows using Apache Airflow, defining complex DAGs with task dependencies, schedules, and error handling to ensure reliable execution of ETL pipelines.
• Implemented CI/CD and continuous training workflows for ML models, enabling automated testing, scheduled retraining, and production monitoring.
• Conducted EDA to support data-driven recommendations on audience targeting, channel mix optimization, and budget allocation.
• Built and integrated RESTful and cloud-based APIs (AWS, FastAPI) and connected to external AI endpoints (OpenAI, Qwen) for model serving, data enrichment, and intelligent automation in production pipelines.
• Analyzed multi-channel campaign performance and delivered insights on attribution and ROI.
• Built interactive dashboards in Power BI, Tableau, and Amazon QuickSight for executive stakeholders, visualizing campaign ROI, engagement trends, and customer segments.

Key Tools & Techniques:

Python, SQL, R, Scikit-learn, MLflow, LLMs, ETL, EDA, NLP, AWS, Docker, CI/CD, Predictive Modeling, RESTful API, Power BI, Tableau.

Project Portfolio:

• AI-Powered Psychologist (NLP Mental Health Assistant): Live Demo
• DataOps Monitoring Platform: Live Demo
• Brands Sentiment Intelligence Dashboard: Live Demo

Responsibilities & Achievements:

• Designed and developed data models, pipelines, and reports for sales, marketing, and production teams using SQL, Python, and Power BI.
• Automated data pipelines for ingestion, preprocessing, transformation, and visualization, reducing reporting time by 50%.
• Conducted exploratory data analysis (EDA) and built interactive dashboards to monitor key KPIs, improving campaign efficiency and visibility.
• Developed and integrated RESTful APIs to consolidate marketing, CRM, and production data into unified KPI tracking systems, enabling real-time performance monitoring.
• Applied machine learning techniques, including predictive modeling and clustering, to improve marketing analytics and production forecasting for data-driven decision-making.
• Delivered measurable business impact: 12.5% reduction in material waste, 90% increase in annual production, 120% growth in lead generation, and 70% rise in sales through data-driven strategy optimization.

Key Tools & Techniques: SQL, Python, Power BI, Tableau, AWS, Google Analytics, Looker, BigQuery, API Integration, Digital Marketing Insights, SEO/SEM.

Key Tools & Techniques:

SQL, Python, Power BI, Tableau, AWS, Google Analytics, Looker, BigQuery, API Integration, Digital Marketing Insights, SEO/SEM.

Responsibilities & Achievements:

• Developed automated dashboards and reports for marketing and sales units using Power BI, Google Data Studio, and DAX.
• Created actionable KPIs (churn, CLV, CPL, conversion rate) to support lead generation and retention efforts.
• Conducted performance analysis on digital campaigns, helping improve targeting and reduce cost per lead.
• Used SQL and Python for customer data analysis, leading to better product bundling and pricing decisions.

Key Tools & Techniques:

Power BI, DAX, SQL, Python, Digital Channel Analytics, Traffic Analysis (G Ads, GA, GSC, Hotjar), Churn Modeling