Vetted Data Analysis Professionals

“People operations and talent acquisition leader who was handpicked to launch the first Google Operations Center in the U.S., building the entire hiring and people-program infrastructure from the ground up (including selecting/configuring Greenhouse). Experienced scaling in new markets using deep talent intelligence and transferable-skill frameworks, and currently advises founders as a board member for a talent acquisition startup with a data-and-benchmarks-driven operating cadence.”

“Senior Program Manager and Chief of Staff at Google who led large-scale cross-functional transformation work, including centralizing program management across a 14,000-person global technical org. Known for building rigorous governance, executive communication cadences, and single-source-of-truth dashboards, while also optimizing executive time and managing sensitive, high-stakes information.”

“COO of Greenopia (pre-seed, pre-revenue) who has built the company’s operational foundation over the past 18 months—supporting fundraising, SEC/crowdfunding setup, and coordinating app and self-serve business onboarding/assessment systems. Brings a metrics-driven operating approach (OKRs, balancing metrics/unit economics) and has advised other startups (e.g., sports betting) to refocus effort on the highest-value customer segments.”

“PhD with 10 years in oil & gas who has repeatedly developed and deployed technologies inside companies, including creating an industry-standard method for mapping oil contamination from the ground surface. Experienced in taking innovations from lab validation through funding, field demonstrations, and regulator/stakeholder alignment, and now looking to found a company building AI tools for heavy industries like oil & gas and mining.”

“Software-focused candidate who built a data monitoring pipeline during a hedge fund internship, integrating real databases and an email API to notify teams when data was ready. Comfortable working through legacy/scrappy code and uses LLMs to accelerate comprehension and delivery, with an emphasis on thorough testing and clear communication with stakeholders/customers.”

“ML/NLP engineer with experience at NVIDIA and Cruise building production-grade AI systems across genomics/biomedical research and autonomous vehicle data. Has delivered multimodal LLM pipelines, large-scale entity resolution, and hybrid semantic search (BERT embeddings + FAISS + Elasticsearch), with measurable impact (≈40% accuracy/retrieval gains; ≈30% data consistency improvement) and strong MLOps practices (Kubernetes, CI/CD, MLflow, Prometheus/Grafana).”

“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”

“Project lead who partners with PM and customers to gather requirements, adjust project plans, and deliver new functionality that drives customer satisfaction and revenue. Has experience building features end-to-end and presenting successful technical demos to engineering and management audiences; no stated experience with LLM/agentic systems.”