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.”

“Consulting-style leader (McKinsey context) driving data- and AI-enabled transformations across healthcare, QSR, retail, and services. Has built AI-powered analytics tools for marketing ideation, led agile operating models with client-side counterparts and external coaches, and navigated highly sensitive competitive retail engagements by isolating teams and delegating pricing work to avoid conflicts.”

“Enterprise cloud/SaaS customer success leader who owned a strategic $102M Azure commitment end-to-end, rescuing a Kubernetes migration by formalizing Sev A/B triage/escalation ownership and executive governance. Drove measurable outcomes including restored executive confidence and consumption growth from $1M to $2M/month (~30% adoption growth) by tying stabilization milestones to migration velocity, forecasting, and expansion strategy.”

“CRO/operator with experience at both AWS and a rapidly scaling Hawaii-based startup (Mana'olana International), focused on building lightweight but scalable operating systems. Known for implementing QBR/OKR rhythms, capacity/territory planning, KPI dashboards, and ROI-tied funding controls, and for mentoring founders through GTM accelerator work (Blue Startups) using a minimum-viable-process approach.”

“Operations/engineering leader from Poly who optimized a professional headset portfolio by aggressively prioritizing technical debt and consolidating HW/SW platforms (down to 2 each), accelerating time-to-market by ~6 months and enabling 3–6 month variant cycles. Also drove AI-enabled quality improvements by leveraging JIRA bug data to automate test case creation and expand coverage, while aligning engineering and product management around MVO delivery.”

“Intel veteran who applied multiple linear regression and time-series drift analysis to semiconductor lithography overlay/metrology data, feeding model outputs into automated process control. Comfortable working across Python, VBA, and JMP/JSL, with a pragmatic approach to validation (RMSE + trend visualization) and data quality via close coordination with measurement/metrology teams.”

“Performance marketing analyst at Meta supporting Amazon who identified Amazon app users as a high-value segment (2x ROAS), built and validated a retargeting audience via rigorous A/B testing, and helped drive a global rollout across thousands of campaigns resulting in ~$80M annual revenue. Strong in experiment design, SQL-driven insight generation, and translating performance learnings into cross-platform creative and catalog strategy.”

“Operations leader with Apple and Roku experience who owned founder-level, cross-functional initiatives including Roku’s Brazil market entry (regulatory, tax/entity setup, manufacturing/logistics, and launch metrics) with direct CEO oversight. Built Roku’s International Business Operations function from zero—standardizing OKRs, dashboards, and country-level P&Ls—and influenced major strategic decisions like exiting a ~$10.5M-loss UK channel business and restructuring Mexico ads go-to-market via partnership.”

“ML/NLP practitioner and technical founder who built an AUP risk-scoring model at Bill.com using TF-IDF + SVD features with XGBoost, and previously created automated data-quality guardrails for a Global Equity Risk stacked ML model at Thomson Reuters. Recently built a RAG-based chatbot for PaymentJock’s Home Affordability Probability product using embeddings and a local vector database (FAISS/Chroma), improving answer quality through chunking rather than expensive fine-tuning.”