Vetted Microsoft Excel Professionals

George Villacis

Screened ReferencesStrong rec.

Mid-level Business Development Manager specializing in financial data for VC/PE

New York, NY6y exp

“Sales/business professional from PitchBook who managed a high-volume pipeline (50–100 opportunities) and drove hundreds of thousands in new business while simultaneously leading cross-functional initiatives, including a product/data dataset implementation and ERG (La Cultura) campaigns. Strong in executive alignment, operational tooling (Salesforce/Asana/Excel), and handling sensitive information with discretion.”

“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”

“Data-centric operator who supports senior executives by anticipating recurring questions, building lightweight systems (meeting cadence/touchpoints), and driving cross-team clarity through rigorous documentation. Recently managed a staffing plan after significant practice turnover, navigating competing principal expectations and capability gaps while maintaining executive trust and positive feedback through proactive level-setting and discretion with PII/PHI.”

“Talent acquisition leader who built and scaled the recruiting team at Houlihan Lokey, ultimately leading ~10–12 recruiters across campus and experienced hiring while partnering closely with HR on offers/comp. Also brings healthcare HR experience from Ketter Medical Center, working with external recruiters to hire physicians, nurses, and administrative staff, and has implemented process improvements tied to DEI/MBA engagement.”

“Atlassian ML/AI engineer who has shipped end-to-end production systems combining classical ML, streaming infrastructure, and LLM-based personalization to improve onboarding and free-to-paid conversion. Particularly strong in turning research-style RAG and reranking ideas into low-latency, reliable product systems with robust evaluation, safety guardrails, and reusable platform services for other teams.”

YAKKALI PAVAN

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems

USA6y exp

“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”

Hanna MacNamee

Screened

Mid-level Client Implementation & Customer Success specialist in FinTech and private markets

New York, NY3y exp

“Client-facing operator with strong pilot conversion and user engagement experience who translates product adoption work into founder-style sourcing and relationship building. They use behavioral data to drive highly personalized outreach, have managed 50-100 outbound touches per week, and have turned disengaged or at-risk users into super-users and referral sources, including in law firm/operator contexts.”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”

“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”

“Gameplay systems designer/engineer with shipped experience across mobile RPG and co-op/AAA shooters, including full ownership of an in-game economy and multiple end-to-end gameplay systems. Strong in data-driven Unreal (C++ + Blueprints) workflows, building flexible subsystems and using simulation/telemetry (Excel/VBA, metrics tools) to tune progression, monetization, and encounter balance.”