Vetted Data Analysts

“Data/ML practitioner with experience at Airtel and Lincoln Financial delivering measurable business outcomes: improved retention 15% via NLP sentiment analysis and cut response time ~25% using sentence-BERT + FAISS semantic linking. Strong in data quality/identity resolution (SQL + fuzzy matching) and in building production-grade Python workflows orchestrated with Airflow/AWS Glue, including validation and dashboard integration in Power BI.”

“Built and scaled an AI-powered voice/chat patient engagement platform at Penn Medicine from early prototype into production clinical workflows, focusing on latency, edge cases, and user trust. Strong in LLM reliability engineering (structured prompts, validation/fallbacks), real-time troubleshooting with observability, and cross-functional enablement through pilots, demos, and sales/customer partnership.”

“Salesforce-focused implementation/solutions engineer from Full Circle Insights who owned end-to-end campaign attribution and reporting deployments for multiple customers at once (3–5 concurrently), including sandbox testing, KPI monitoring, and rollback-safe migrations from legacy reporting. Also builds personal multi-agent workflows and uses Claude Code to rapidly scaffold data/analytics scripts like an advertising optimization parser over CSV/XLSX inputs.”

“AI/LLM engineer who built a production intelligent support system using RAG over a vectorized documentation library, addressing real-world issues like lost-in-the-middle context failures and doc freshness via automated GitHub-driven re-embedding pipelines. Emphasizes rigorous agent evaluation (component/E2E/ops) and prefers lightweight, decoupled workflow automation using message brokers (Redis/RabbitMQ) over heavyweight orchestration frameworks.”

“IBM engineer who built and deployed a production RAG-based LLM assistant using LangChain/FAISS with a fine-tuned LLaMA model, served via FastAPI microservices on Kubernetes, achieving 99%+ uptime. Demonstrates strong practical expertise in reducing hallucinations (semantic chunking + metadata-driven retrieval) and managing latency, plus mature MLOps practices (Airflow/dbt pipelines, MLflow tracking, monitoring, A/B and shadow deployments) and effective collaboration with non-technical stakeholders.”

“Built and deployed a production LLM-powered financial risk and compliance platform to reduce manual trade exception handling and speed up insights from regulatory documents. Implemented a LangChain multi-agent workflow with structured/unstructured data integration (Redshift + vector DB) and emphasized hallucination reduction for regulatory safety using Amazon Bedrock. Strong MLOps/orchestration background across Kubernetes, Airflow, Jenkins, and monitoring/testing with MLflow, Evidently AI, and PyTest.”

“Data Software Tools Analyst at Q.ai through rapid growth and a $2B Apple acquisition who led an internal CMS for participant/PII workflows using Next.js (App Router) + FastAPI/Postgres with strong security controls (JWT + Postgres RLS). Also drove a major frontend architecture shift toward React Server Components, reporting ~4x faster page loads, and has experience building durable realtime collaboration systems with Supabase/SvelteKit and server-centric state management.”

“Built a production "appeals co-pilot" for a healthcare claims appeals team, combining an XGBoost/logistic ranking model with a Python/LangChain RAG stack (FAISS + Mistral 7B) to surface high-probability appeal wins and speed policy-grounded drafting. Emphasizes reliability and trust: hybrid retrieval with metadata routing, citation/eval scripts, guardrails, and an explainability layer that non-technical stakeholders could understand and override.”

“Data/ML practitioner with experience at UnitedHealth Group building a fraud claims detection solution combining structured claims data and unstructured notes, validated with compliance stakeholders to improve actionable accuracy. Also applied embeddings, vector databases, and fine-tuned language models in a Bank of America capstone to detect threats/anomalies in financial documents, with production-minded Python ETL workflows using Airflow.”

“Engineering leader focused on developer platforms and identity: led a 5-person team building a multi-tenant IAM framework extending Duende IdentityServer, including governance, API standards, and multi-language SDKs. Drove major API architecture improvements by introducing GraphQL alongside REST, cutting client calls from 4 to 1 and reducing payload size by 60% within 6 months, with strong emphasis on stability (semver, E2E tests, feature flags, blue-green deploys).”