Vetted Forecasting Professionals

“18-year Google veteran who managed and grew top customer investments, including Rocket Mortgage’s Google Search program—scaling spend from ~$60M/year to $170M/year. Led adoption of ROAS bidding and broad match through rigorous experimentation and forecasting, driving an additional $14.5M spend in 1H 2024 despite a challenging mortgage market.”

“Software development manager who led development of the open-source AI agents framework Strands (for Amazon Bedrock), which launched mid-year and is used as a primary agent-development framework at Amazon and externally. Owned the Strands SDK end-to-end, drove adoption through stakeholder management and agile execution, and led a JavaScript-to-TypeScript migration to improve type safety and maintainability.”

Christopher Sipola

Screened

Senior Machine Learning Engineer specializing in LLMs and recommendation systems

New York, NY11y exp

“ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.”

Franklin Dickinson

Screened

Senior Data Scientist specializing in product analytics and applied AI

New York, NY9y exp

“Product data scientist at Meta who also built Fogo, a personal finance app, as a solo developer with a React/FastAPI/Postgres stack and sophisticated LLM evaluation infrastructure. Particularly strong in AI backend quality systems, experimentation, and production decision-making around model cost, latency, and reliability.”

“Entrepreneur building an AI-driven small molecule biotech startup who has already raised $5M from major venture capital investors. Brings 3 years of fundraising experience and a thoughtful approach to aligning investors with different risk profiles across financing stages, paired with strong conviction around founding high-risk, high-upside companies.”

“Research-oriented ML engineer/scientist with deep experience applying generative models, adaptive optimization, and HPC infrastructure to complex physics analyses. Built reusable Python-based tools that replaced expensive Monte Carlo workflows, integrated across HTCondor/SLURM environments, and reduced analysis timelines by 2x while supporting broader team adoption and training.”

“Engineering leader with deep distributed-systems expertise who led Oracle's replacement of a legacy Fusion Applications authorization service with a cloud-native Go/Kubernetes RBAC platform delivering sub-10ms latency. Combines hands-on architecture depth with org-building experience, having grown a geo-distributed team from 4 to 26 and led managers, architects, and cross-functional delivery across Oracle and Microsoft environments.”