Vetted Statistical Analysis Professionals

“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”

“AI/LLM practitioner who has deployed production chatbots across e-commerce, HRMS, and real estate, focusing on retrieval-first workflows for factual tasks like product and property search. Optimized intent understanding and significantly improved latency by using lightweight embeddings and tuning the inference pipeline on Groq (Llama 3.3), while applying modular orchestration and measurable production evaluation.”

Jeremy Wright

Screened

Executive Marketing Leader / Fractional CMO specializing in strategy, branding, and growth

Barrie, Canada16y exp

“Growth/partnership leader spanning gaming, creator ecosystems, and consumer/B2B GTM with unusually strong network reach (senior contacts across major tech). Built a white-label game hosting partner channel at Shockbyte to $5MM+ net new ARR with minimal investment, and previously conceived/negotiated a globally scaled Kinect experiential retail strategy attributed to 1M+ units and ~$500MM revenue. Also led FreshBooks’ early GTM and scaling from ~$500K to $15MM+ annual revenue while growing teams from 3 to 50–75.”

Meng Yang

Screened ReferencesStrong rec.

Staff Software Engineer specializing in distributed systems, cloud platforms, and IoT

Columbus, OH21y exp

“CTO/Chief Architect who rebuilt an IoT platform from a fragile legacy stack into an AWS-based, multi-tenant cloud-native system supporting 50k+ connected devices and 10M+ monthly events, then layered in real-time data pipelines and ML anomaly detection. Known for tightly aligning roadmaps and OKRs to business KPIs (onboarding speed, uptime, velocity) and for scaling teams into domain-focused pods; previously led a shift from LAMP to event-driven Node.js microservices using MQTT and message queues.”

“Analytics professional with hands-on experience spanning enterprise supply chain data and workforce analytics. They’ve worked on a Manhattan Active WMS implementation for a pharmaceutical client integrating MAWM, JD Edwards, and Boomi, and also built SQL/Python/Tableau solutions for BankUnited/FIU to standardize retention and engagement reporting. Strong fit for roles requiring messy data wrangling, KPI operationalization, and stakeholder-trusted dashboards.”

“Healthcare analytics professional with hands-on experience turning messy claims, eligibility, and utilization data into validated BI-ready models using SQL and Python. They combine strong data engineering and KPI design skills with stakeholder-facing delivery, including Power BI prototyping, retention metric operationalization, and analyses that supported care management interventions and cost-control decisions.”

“AI Engineer with production experience building an LLM-powered conversational scheduling assistant (rules-based + OpenAI GPT agents) and improving responsiveness by ~40% through architecture optimization. Strong in orchestration (Airflow), containerized deployments, and data quality (Great Expectations/PySpark), with prior work automating population health reporting pipelines (Azure Data Factory → Snowflake) and delivering insights via Tableau to non-technical stakeholders.”

“Data scientist at Guardian Airwaves building a RAG-powered quiz generator using Grok AI, with hands-on experience solving hard document-ingestion problems (PDFs with images/tables) via unstructured.io and LlamaIndex. Has deployed production systems on AWS EC2 and brings a pragmatic approach to agent reliability (human-in-the-loop, LLM-based eval, latency/cost metrics) while effectively translating RAG concepts to non-technical stakeholders.”

“Hands-on backend engineer with practical experience improving latency in Django-based API systems by fixing missing indexes and eliminating N+1 queries. Also built an AI scheduling system using FastAPI, a relational database, AI/ML workflows, and an operational reporting dashboard, with a clear bias toward correctness and maintainable architecture.”

“Healthcare-focused ML/AI engineer who has built clinical note summarization and medical image annotation solutions using LLMs, RAG, and multimodal models. They combine experimentation across major model providers with practical production concerns like monitoring, drift detection, and latency/cost tradeoffs, and also earned 2nd place in a Google hackathon for a medical AI assistant.”

Sasi Kanduri

Screened

Senior Full-Stack Software Engineer specializing in backend systems and workflow platforms

Sacramento, CA7y exp

“Full-stack engineer with strong React and Python backend depth who has owned complex analytical products end-to-end, from performant UIs to FastAPI services, SQLAlchemy data models, Redis caching, and production observability. Particularly compelling is their 0→1 automation work in the water systems domain, where they built Airflow- and LLM-powered workflows that reduced manual notification and correction work by 90%.”

“Software engineer currently building for a wealth management platform, with hands-on ownership across React/TypeScript front ends, Azure serverless backends, and SQL data systems. Stands out for turning hackweek concepts into production features, building observability infrastructure from scratch, and delivering measurable gains in latency, uptime, incident response, and client satisfaction.”

“Built and shipped a production GPT-4 + RAG customer support chatbot that materially improved support operations (response time 4 hours to <3 minutes; ~65% tier-1 ticket automation). Demonstrates strong end-to-end LLM engineering across retrieval (Sentence Transformers/Pinecone), safety (multi-layer moderation), cost/latency optimization (caching/streaming, Celery/Redis), and rigorous evaluation/monitoring (shadow deploys, Datadog, 500+ test cases), plus proven stakeholder buy-in leading to 80% adoption.”

“Built a production "Mini RAG Assistant" for internal document Q&A, focusing on grounded answers (anti-hallucination), retrieval quality, and latency/cost optimization. Uses LangChain/LangGraph for orchestration and applies a metrics-driven evaluation loop (including reranking and semantic chunking improvements) while collaborating closely with product stakeholders.”

“Accenture engineer who built and shipped a production RAG-based automation/chatbot for SAP incident triage and troubleshooting, embedding thousands of runbooks/logs/tickets into a semantic search pipeline and integrating it into Teams/Slack. Reported major productivity gains (30–60% time reduction), >90% validated answer accuracy, and sub-2-second responses, with strong orchestration (Airflow/Prefect/LangGraph) and reliability practices (guardrails, testing, monitoring).”

Michelle Jimenez

Screened

HR Director specializing in HR strategy, benefits/payroll, and industrial security programs

New York, NY21y exp

“People/HR leader focused on retention, engagement, and organizational effectiveness—partnering with executives to implement performance review systems, redesign teams for growth, and improve hiring/onboarding. Brings strong employee relations experience (including harassment investigations) and uses people metrics and market research to optimize benefits and drive participation.”