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.”

Monica SilvaGutierrez

Screened

Executive Operations & Chief of Staff leader specializing in org change, People Ops, and operational excellence

New York, NY18y exp

“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.”

Jon Cross

Screened

Executive Operations & Product Leader specializing in Ads, customer experience, and go-to-market

Saratoga, CA21y exp

“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.”

Kevin Wang

Screened

Senior Software Engineer specializing in cloud infrastructure and distributed systems

Adams, MA8y exp

“Amazon engineer focused on productionizing LLM-powered developer workflows, including code assistance, debugging automation, and internal AI tooling. Stands out for combining hands-on ML systems work with strong platform engineering, including an orchestration engine that reportedly saved about $10K/day and reduced a manual workflow from 12 hours to under a second.”

“Worked on an AWS DynamoDB Journal team project building internal operator dashboards end-to-end, creating Java/Spring Boot APIs and integrating them into a Spring Boot/Thymeleaf/JavaScript UI to speed up debugging workflows. Has experience with data-heavy web apps and performance techniques (load balancing, caching, pagination, compression) plus hands-on debugging across unit/integration/E2E tests; also maintained and enhanced a React website at Global Spark.”

Ridhi Singhal

Screened ReferencesStrong rec.

Mid-level strategy and analytics professional specializing in growth and due diligence

Atlanta, GA4y exp

“Growth and launch operator with Uber experience spanning marketplace expansion, rider/driver activation, and scalable onboarding systems in new-city launches, including Uber Shuttle in India. Stands out for combining data analysis, operational modeling, and cross-functional experimentation to unlock incremental revenue, improve NPS, and build repeatable playbooks that scaled across markets.”

“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.”

Denis Siminiuc

Screened

Junior Research Assistant specializing in LLMs, NLP, and data systems

Cambridge, MA3y exp

“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).”

Brian Gomez

Screened

Senior Machine Learning Engineer specializing in AI/ML, NLP, and computer vision

United States12y exp

“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).”