Vetted Data & Analytics Professionals

“McKinsey consultant (BA and newly promoted Engagement Manager) with experience leading large-scale operating model and digital transformations using stage-gate governance and executive steering committees. Also served as a Policy Advisor to a governor, producing confidential executive briefings and implementing tracking systems to drive interagency accountability on climate and resilience initiatives; delivered a pharma/biotech workforce roadmap projecting $75M savings over 3 years.”

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.”

“Early-stage founder building a service business targeting small clinics, already with one client. Identified the opportunity by helping a family member and then validating needs through direct client conversations; uses AI (including AI agents) for content generation and plans deeper workflow automation to scale cost-effectively.”

Chavez Yi Wei Cheong

Screened

Mid-level analyst and software engineer specializing in AI, distributed systems, and product delivery

San Francisco, CA5y exp

“Engineer with end-to-end shipping experience spanning large-scale infrastructure at Meta and scrappy startup automation work. Built a C++ streaming platform for Facebook Marketplace with canary and chaos testing, and also created Selenium-based scraping and GTM automation workflows under ambiguous, real-world conditions.”

“Senior product engineering and data architecture leader who has owned end-to-end solutions for enterprise analytics and legal search platforms, including work for Sony Pictures Entertainment and Accenture Legal. Combines executive-level architecture and team leadership with hands-on experimentation in modern AI systems, recently building a multimodal Hugging Face agent that significantly exceeded its benchmark score.”

“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”

“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.”

Abhinav Bandaru

Screened

Junior Data Scientist specializing in Generative AI and agentic LLM systems

San Jose, CA1y exp

“LLM/agentic-systems builder who has shipped production tools for investment research and procurement insights, including a company screener that processes thousands of conference-listed companies using FireCrawl + Google Search + Gemini. Demonstrates strong orchestration expertise (LangGraph multi-agent graphs), performance optimization (async/batching to sub-30s), and pragmatic reliability/evaluation practices with stakeholder-friendly UX (real-time cost tracking and model/parameter toggles).”

“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.”

“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.”