🏢 Professional Experience

Advised a media client on user acquisition, marketing analytics and improved marketing channel ROI.
Developed multi-touch attribution data model & established business KPI’s to measure ad campaign effectiveness that helped optimize marketing spend.
Performed causal inference using synthetic control generation for incrementality measurement of ad campaigns.
Leveraged advanced statistical techniques to analyse trends in viewership behaviour for audience segmentation.
Optimized audience matching data pipeline for high dimensional attributes & increased response time by 65%.
Worked closely with data scientists to identify data needs and testing supervised machine learning models.
Designed & maintained 12 self-serve dashboards that empowered stakeholders to drive real time optimizations.
Built necessary data pipelines to power automation that eliminated manual intervention of 3 hours.
Led a summer intern towards acheiving their goals and thereby helping them to develop their skills.

Developing strategies based on insights from podcasts transcriptions and podcast data for host read ads.
Text Analytics to derive insights from large podcast episode transcriptions data with Spark big data processing.
Built a data collection workflow using Python & Google Cloud (Speech To Text, Cloud Storage, MySQL) for loading data to data store.
Implemented web scrapers responsible for extracting relevant information from RSS feeds & web API endpoints.
Implemented a relational database model to include information relevant for building analytic data store.
Led a team of three , ensuring high quality deliverables and timely work.

Deployed reporting solutions over ~5M active subscriber base for a Telecom client to make business decisions.
Designed Business Intelligence reports using SSRS and implemented SSIS ETL pipelines for finance & ad-hoc reporting.
Coordinated with client to gather requirements to build BI reports for monitoring revenue and service usage.
Analysed the variances in report reconciliations that uncovered 5% of new system issues and fraudulent transactions.
Implemented KPI’s to measure performance of system jobs and analyse patterns in defects.
Built a Tableau dashboard summarizing customer service performance with case level , individual agent & team level metrics.
Optimized SQL queries with window functions that improved turnaround time of a finance reports from 1.5 hours to 30 mins.
Automated daily, weekly & monthly financial reporting activity using Python that eliminated manual process of over 1 hour.