Jan 2025 – Apr 2025 · 3 mos · Toronto, Ontario, Canada · Remote

• Optimized Data Ingestion & BI Architecture: Improved an end-to-end automated pipeline using Python and AWS S3 to centralize CRM, ERP, and Marketing Data Marts into Snowflake. Developed Power BI dashboards for C-suite stakeholders, replacing manual weekly reporting with real-time automated refreshes.
• Predictive Revenue Modeling (CLV): Engineered a Customer Lifetime Value (CLV) forecasting model using Python (Scikit-learn). By analyzing historical transactions and engagement metrics in Snowflake, I reduced projection errors (MAE), enabling the marketing team to optimize budget allocation toward high-value segments.
• Anomaly Detection & Risk Mitigation: Deployed machine learning algorithms to monitor platform security logs, increasing incident identification accuracy by 28%. Implemented automated risk scoring to detect and mitigate fraudulent transactions—a core framework for maintaining platform integrity.
• Data Governance & Standardization: Led the standardization of enterprise data protocols and KPI dictionaries within the COE using Jira and SharePoint. Improved cross-functional data consistency by 40%, significantly streamlining audit readiness for 10+ new product launches.

Mar 2024 – Present · 2 yrs 3 mos · Etobicoke, Ontario, Canada · On-site

• Operational Reporting & Coordination: Managed daily administrative data and facilitated clear communication between club management and staff. Ensured all operational reports and member records were accurate and delivered on time for internal reviews.
• Member Engagement & Data Integrity: Tracked member check-ins and engagement trends to support local sales initiatives. Maintained high data integrity in the CRM system, ensuring member records were structured and up-to-date for accurate performance tracking.

May 2023 – Dec 2023 · 7 mos · Bengaluru, Karnataka, India

• Enterprise ETL & Latency Optimization: Engineered enterprise-scale ETL pipelines using Airflow, SQL, and Python; automated data mapping for 75% of the warehouse and slashed data access latency by 50%, accelerating delivery for cross-functional engineering teams.
• Executive BI Architecture: Deployed 40+ client-facing Tableau dashboards, integrating disparate data sources to reduce reporting Turnaround Time (TAT) by 80%. Eliminated cross-platform discrepancies to provide executive stakeholders with a "single source of truth."
• Geospatial & Strategic Modeling: Developed geospatial demand models for partners like Uber and Amazon using Python (Scikit-learn). Optimized regional targeting strategies that delivered a 25% uplift in customer acquisition and improved resource allocation efficiency.
• Fraud Detection & Security Analytics: Architected classification models to detect anomalous spending behavior. By identifying high-risk fraud signals, I enhanced platform integrity and data security protocols, directly impacting client trust and regulatory compliance.
• Behavioral Segmentation & ROAS Optimization: Leveraged K-means clustering to analyze user behavior for 12+ monthly campaigns; identified high-intent patterns that drove a 15% increase in conversion rates and significantly improved Return on Ad Spend (ROAS).

Aug 2022 – May 2023 · 9 mos · Bengaluru, Karnataka, India

• Statistical Experimentation & A/B Testing: Designed and executed rigorous statistical tests, including A/B testing and impact analysis, to quantify product performance. Utilized Python (SciPy/Pandas) to measure business impact, ensuring all product rollouts were backed by data-driven confidence intervals.
• Automated Reporting Infrastructure: Architected and maintained end-to-end automated reporting pipelines, eliminating manual intervention for monthly delivery. Ensured 100% data consistency across product and business stakeholder layers by implementing automated validation checks.
• Advanced Data Visualization (Superset/Plotly): Developed high-fidelity interactive dashboards using Apache Superset and Plotly. Translated complex multi-dimensional datasets into clear, actionable visual intelligence, enabling organization-wide access to real-time performance trends.
• Strategic Stakeholder Intelligence: Delivered recurring, insight-driven executive briefings used by product leadership to steer long-term strategy. Focused on translating technical data anomalies into business-centric narratives to support high-stakes decision-making.

Jan 2022 – Jul 2022 · 6 mos · Chennai, Tamil Nadu, India

• Real-Time Data Architecture: Developed WebSocket-based components using advanced data structures to facilitate high-frequency, real-time data communication. This optimized system responsiveness for live data feeds and reduced latency in synchronization.
• Knowledge Graph Modeling (Neo4j): Designed and managed conceptual graph data models in Neo4j to map complex AI-driven data relationships. This allowed for deeper link-analysis and more sophisticated pattern recognition compared to traditional relational databases.
• Unstructured Data Engineering: Engineered automated scripts in Python (Regex, Pandas) to parse and transform complex JSON and unstructured PDF records into production-ready datasets, eliminating 100+ hours of manual processing monthly.
• Relational Migration & RDS Optimization: Facilitated large-scale data migrations from Graph (Neo4j) to Relational (PostgreSQL/AWS RDS) environments. Implemented automated validation logic to ensure 100% data integrity and enhanced audit traceability for sensitive financial records.

May 2021 – Dec 2021 · 7 mos · Gurugram, Haryana, India

• Fraud Mitigation & Behavioral Modeling: Built advanced analytical frameworks to mitigate platform risk, reducing mass-order fraud by 25%. Leveraged Root Cause Analysis (RCA) and behavioral data modeling in Python to detect irregular patterns and strengthen fraud monitoring initiatives.
• Serverless Workflow Automation (AWS): Digitized manual redemption workflows and monthly reporting using AWS Lambda and S3. Reduced manual touchpoints by over 90% for partner communications and OLTP transactional records at full operational scale.
• Production-Level Data Pipelines: Developed and deployed production-ready pipelines using Python and Google Service Accounts to automate the extraction of partner data. Implemented Cron jobs for daily execution, ensuring 100% data availability for executive morning reporting and eliminating manual downloads.
• Operational Risk & Integrity: Conducted deep-dive data validation and anomaly detection to identify system inconsistencies. Improved overall data reliability and system integrity, providing a secure foundation for high-stakes operational decision-making.