Chicago, Illinois, United States
I bring a rare edge to Go-To-Market: a data science foundation that turns ambiguity into pipeline. At Simular, I lead GTM for an AI product — from demand generation and content strategy to conversion optimization and sales enablement. Previously at Kuse, I built the GTM function from scratch, and at AMA Career, I drove a 20% increase in paid user conversion through A/B testing and data-driven campaign optimization. What makes me different: I don't just run campaigns — I build the dashboards, analyze the funnel, and optimize with precision. My background spans data science (Python, SQL, ML), product analytics, and growth marketing across AI, edtech, and B2B SaaS. What I'm looking for: GTM, growth, or demand generation roles at early-stage AI/tech companies where data-driven thinking and scrappy execution matter. Let's connect — DM me here on LinkedIn!
1. Designed and deployed real-time dashboards leveraging advanced SQL, enabling the tracking of key metrics for two core products, and driving a 15% increase in feature adoption through data-driven prioritization 2. Conducted quantitative user research via Qualtrics and dashboards, identifying bottlenecks in the user journey and improving user engagement by 10% through targeted onboarding optimizations 3. Built and automated end-to-end pipelines connecting backend data processing to frontend dashboards, reducing manual reporting time by 15 hours per month and enhancing team productivity. 4. Conducted A/B testing across multiple marketing channels to evaluate campaign effectiveness, optimizing strategies and achieving a 20% increase in paid user conversion rates.
1. Engineered a coding framework to manually extract visual features from 40,000+ multimodal AIGI social media posts, achieving a 93% accuracy in misinformation detection and reducing manual review time by 30%, streamlining large-scale content analysis processes. 2. Applied K-Prototype clustering to segment AIGI datasets, improving classification accuracy by 20% and identifying 12 critical misinformation-linked features, which informed strategies to mitigate false positives, reducing misinformation detection errors. 3. Designed and deployed an image detection model using Amazon Rekognition on a dataset of 1,000+ images, enabling comparison between AI-driven and manual feature extraction methods, and uncovering 5 key discrepancies for further optimization.
1. Constructed large-scale database based on political-economic reports, conducting auditing tests across 10+ multilingual Large Language Models (LLMs); automated 400,000+ rounds of response testing, identifying political bias patterns. 2. Designed and deployed a Python module using object-oriented programming and Hugging Face Inference Client API, optimizing LLM inference pipelines and increasing script execution efficiency by 30%, streamlining model testing workflows 3. Leveraged Natural Language Processing (NLP) to create sentiment analysis models, accurately predicting users’ sentiment towards political events on social media with 86% precision, informing policy recommendations. 4. Leveraged business intelligence tools like Tableau to present key metrics and progress charts weekly, presenting key metrics and progress to increase the efficiency of decision making process by 30%.