Professional Summary
Data & Analytics Consulting Engineer with 2.5+ years delivering digital transformation, AI-enabled products, and data solutions across enterprise operations. Currently a Product Manager Intern at IpserLab LLC in Los Angeles, leading GenAI/LLM integration strategy and driving product requirements for an internal platform — while completing an MS in Information Systems at Pace University's Seidenberg School (Data Science & Analytics, 3.8 GPA).
Previously a Data Strategy Analyst at the MTA, building Power BI dashboards and GIS pipelines on Azure Data Lake to support enterprise asset management. Before that, at Ernst & Young, engineered 20+ ETL pipelines and deployed AI-enabled SCM solutions on the EY ASTERISK platform, cutting time-to-market by 20% and reducing infrastructure costs by $18K annually for Fortune 500 clients across Automotive and FMCG.
AWS Solutions Architect Associate certified and McKinsey Forward Program alumnus. Technical toolkit spans Python, SQL, Azure, Databricks, Airflow, Power BI, Tableau, and Agentic AI. Bridging data engineering and product strategy to turn complex requirements into measurable outcomes. Open to 2026 full-time roles in Data Engineering, Product, Analytics, and Technology Consulting.
Core Competencies
Professional Experience
For complete work history refer to my curriculum vitae.
Product Manager Intern
Current- Lead product requirements for an internal platform, structuring business goals and engineering constraints into a PRD, user stories, and acceptance criteria that anchor a prioritized Agile backlog and product roadmap, tracked in Jira/Confluence.
- Drive GenAI/LLM integration strategy, evaluating use cases against system architecture and user needs, and designing the UAT framework that validates each release against stakeholder requirements, resolving mid-sprint blockers with engineering.
Data Strategy Analyst – EAM, Strategic Initiatives
- Analyzed operational and asset datasets using SQL and AI-assisted analytics, applying GenAI tools for data manipulation, cleansing, and transformation across enterprise asset workflows to surface efficiency opportunities that improved decision-making accuracy by 20%+.
- Designed 3+ data models, Power BI dashboards and data frameworks on Azure Data Lake and enhanced GIS data pipelines within HxGN EAM, improving data quality and real-time asset visibility across enterprise reporting workflows.
- Streamlined cross-functional governance using Jira & Confluence, improving project alignment & performance by 25%.
Advanced Analyst, Staff II – Business Consulting, Analytics & Strategy
- Spearheaded the EY ASTERISK SaaS platform's digital transformation, crafting product roadmaps and GTM strategies for AI-enabled SCM solutions.
- Orchestrated 45+ high-performance ETL data pipelines (Python, SQL, Azure Databricks) powering analytics for Fortune 500 clients, improving data accuracy by 30%.
- Deployed 10+ Power BI dashboards integrating KPIs and Geo-based vendor data, driving a 35% boost in operational efficiency.
- Drove CRM integration and customer experience analytics, partnering with C-suite stakeholders and streamlining end-to-end product strategy.
Data Operations Analyst
- Designed & implemented cloud-based data pipelines for legal eDiscovery forensics, enforcing data privacy compliance across 5,000+ legal records.
- Built data warehousing solutions and automated analytics workflows, improving pipeline efficiency by 20% and analytical output by 30%.
Education
MS, Computer & Information Systems
Data Science Engineering & Analytics | GPA: 3.8
BS, Computer Science
Applied CS, Cloud, Data & Analytics | GPA: 3.64
Projects, Research & Certifications
Certifications
Publications
Academic Research
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"The Suggestive Banker"
Capstone: Consumer Trust in Agentic AI for Digital Banking · 2026
Projects
SmartChurn – Credit Card Attrition Prediction
Mar 2025 – Present
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- Conducted end-to-end development of a customer churn prediction system for Great One Bank using a 10K-record dataset, integrating Tableau-driven EDA and Python-based machine learning.
- Built and validated a high-performance CatBoost classification model, achieving 97.17% accuracy, 90.77% precision, and 92.48% recall, outperforming baseline models.
- Addressed class imbalance using SMOTE and hyperparameter tuning; final production test applying trained model to 100,000+ customers underway.
HeartBeacon – Cardiovascular Disease Prediction
Nov 2024 – Dec 2024
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- Engineered a heart disease prediction pipeline using Python and a 319,000+ record dataset from Kaggle, achieving 90% accuracy through robust preprocessing and data balancing techniques.
- Built and evaluated classification models including Logistic Regression, Decision Tree, and Random Forest; optimized for recall in minority class using random over-sampling.
- Implemented EDA and data visualization using Seaborn and Matplotlib, uncovering key clinical insights like the impact of BMI, sleep time, and mental health on heart disease.
Let's Connect
Actively seeking 2026 full-time opportunities in Data, Product, and Tech Strategy.