Work

Moloco

May 2025 — Aug 2025

• Analyzed monetization patterns across 35 RMG apps ($64.4M+ spend); uncovered iOS LTV delays and post-D30 revenue concentration, guiding long-horizon ROAS modeling.
• Built an anomaly detection engine using rolling z-scores to flag app-level shifts; informed $8M+ in campaign optimizations including DraftKings, Betr, and FanDuel.
• Diagnosed an 80% spend drop and 2× CPA rise in DraftKings Android campaigns; identified conversion inefficiencies and creative imbalance as key drivers.
• Modeled ARPPU trends across OS and verticals; revealed 3–5× iOS monetization advantage, driving budget shifts to high-value segments.

Moloco

May 2024 — Aug 2024

• Increased win rates by 5% and actions by 30% for AMR consumer advertisers by adjusting discount rates dynamically for high-value users. Boosted CPA predictability by 20% via statistical research on creative diversity, funnel category, and budget mode, leading to actionable A/B test recommendations.
• Achieved a 3–4% increase in ROAS for Activision by correlating pLTV with purchase events, leading to better revenue performance in D3 pLTV campaigns.
• Developed DMA-based strategies for Bumble that reduced CPA by 15%, identifying optimal spend levels and high-performing DMA markets, enhancing profitability for UA campaigns.

Sabre Labs

Jan 2024 — May 2024

• Led the development of a robust LLM-based solution leveraging LLaMA 2 to unify address structures, processing 1.35 million records of lodging properties from diverse aggregators. Achieved an initial accuracy score of 82.7%.
• Utilized a BERT-based model to generate contextual embeddings from categorical data in over 6 million shop requests to Sabre IntelliSell, improving accuracy and performance for cache rate prediction.
• Used the generated contextual embeddings for dynamic price prediction as a downstream task.
• Leveraged the HPC capabilities of the TACC-Lonestar 6 supercomputer for parallel computing and optimization.

Twimbit

Apr 2022 — Jul 2022

• Data Analysis: Installed and managed a holistic data pipeline (Algolia, Heap, Matomo, Segment) for tracking website user interactions to facilitate data-driven decisions.
• Leveraged A/B test insights and ad-hoc analysis to reduce product friction and boost daily user numbers by 5%.
• Machine Learning: Parsed raw HTML data from 700+ webpages on the product website using Beautiful Soup to train a Decision Tree model for automated classification of records into distinct categories.
• Proposed and implemented a unique metric correlating read time to page depth scrolled, improving page readability and user retention by 20%.
• Utilized text processing and topic modeling using gensim and spacy-transformers, leading to a 64% improvement in search query response time.