Vetted A/B Testing Professionals

“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”

“UX/Product designer with agtech internship experience designing an end-to-end platform, including user journeys, interaction models, and a clean visual design system. Has hands-on field research experience on farms that directly shaped mobile-friendly UI decisions (one-handed, on-the-move workflows) and a front-end development background (React/HTML/CSS/JS) that supports smooth engineering handoff.”

“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”

“Growth/marketing operator with hands-on experience across creator partnerships and performance acquisition. Sourced creators via TikTok Creator Marketplace and qualitative engagement checks, negotiated performance-based deals, and achieved a 15% CPA improvement that expanded into a long-term ambassador program. Also led persona-based GTM and funnel experiments at Haletale, driving a 20% lift in demo bookings without increasing budget.”

“Full-stack product engineer with a strong real-time systems focus: built and rolled out a WebSocket-based notifications system (with robust reconnect/resync and event ordering protections) that cut update latency to under 200ms. Also owned a workflow automation platform backend in FastAPI (JWT/RBAC, versioned APIs, standardized errors), designed the PostgreSQL schema for workflows/tasks/executions, and operated deployments on AWS ECS Fargate with blue-green CI/CD and performance stabilization via caching and autoscaling.”

“Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).”