Work Experience
Full Time
Data Analyst | Feb 2021 - Dec 2022
Freshworks
- - Developed SQL and Python pipelines that supported analytics for support operations, resulting in a 30% reduction in manual data handling
- - Collaborated cross-functionally with Product and Customer Success teams to design A/B tests; results guided product feature prioritization
- - Created in-depth customer engagement dashboards with PowerBI, DAX and MySQL to identify support process bottlenecks, providing a real-time view of KPIs and aiding optimisation strategies which led to a 15% increase in productivity
- - Implemented CRM and Marketing workflow automations, resulting in 25% productivity boost for multiple clients.
- - Built an advanced financial analytics system to track Annual Recurring Revenue (ARR), enabling the Finance team to optimize revenue forecasting and streamline book closure activities
- - Setup major Zapier and Snowflake integrations for internal projects as well as for several clients on request
- - Trained and mentored 3 analysts, fostering a culture of collaborative analytics and best practices in storytelling with data
Work Integrated Learning
Data Science Engineer | RMIT Marketing Ad Analytics Platform | Aug 2024 - Oct 2024
RMIT University
- - Led a data-driven ad analytics project for RMIT's marketing team, improving digital campaign click-through rates by 10%. This involved analyzing campaign performance (Google Ads), developing web-based dashboards to visualize key metrics, and providing recommendations for A/B testing ad creatives and targeting strategies.
- - Designed and architected an efficient, event-driven, Python-based platform, resulting in 50% reduction in AWS costs, specifically by switching to RDS from DynamoDB
- - Reduced research cycle time by 90% by automating ad collection, processing, and insight extraction through an ETL pipeline.
- - Owned auth and data visualisation modules and delivered a secure platform with Role-Based Access control
- - Optimised API response times from 25 seconds to under 4 seconds, resulting in a 5x performance improvement in the competitor analytics module
- - Achieved a 99% retrieval precision rate, ensuring that most top-N results were relevant to user queries, by utilising PGVector in Postgres and text embeddings instead of raw text-based search
- - Streamlined CI/CD processes, cutting deployment errors by 80% using GitHub Actions