Vetted Data Visualization Professionals

“Serial entrepreneur since leaving corporate in 2009, working largely for equity on multiple startups. Building (1) academically rigorous, anti-overfitting quant/backtesting tools for retail investors (with potential applicability to smaller hedge funds lacking quant staff) and (2) a partner-led “social-as-a-service” platform for verticals like real estate/PropTech (including FSBO use cases) focused on first-party data capture vs. big tech.”

“Candidate states they are very familiar with the venture capital/studio/accelerator landscape and expresses strong willingness to pursue entrepreneurship "at all costs," but did not provide details on a current startup, business plan, fundraising, or prior accelerator/VC involvement during the interview.”

“Robotics software/ML engineer who built perception and navigation-related ML systems for autonomous supermarket carts, including object detection, shelf recognition, and obstacle avoidance. Strong ROS/ROS2 practitioner who optimized real-time performance (reported 50% latency reduction) and deployed containerized ROS/ML pipelines at scale using Docker, Kubernetes, and CI/CD.”

“UK-based Unity developer who built a 3D simulation/digital-twin platform for an autonomous-vehicle startup, integrating Unity environments with external robotics stacks, web APIs, virtual sensing, and dynamic traffic systems. Interested in moving into VR, though has not shipped VR/Meta Quest titles yet.”

“GenAI/ML engineer with production experience at Cognizant and Ally Financial, building end-to-end LLM/RAG systems and ML pipelines. Delivered a domain chatbot trained from 90k tickets and 45k docs, improving intent accuracy (65%→83%), scaling to 800+ concurrent users with 99.2% uptime and sub-150ms latency, and driving +14% customer satisfaction. Strong in Azure ML + DevOps CI/CD, Dockerized deployments, and explainable/PII-safe modeling using SHAP/LIME to satisfy stakeholder trust and GDPR needs.”

“Backend/web API engineer with hands-on experience deploying .NET Core APIs to Azure App Service and stabilizing production systems through disciplined log-driven troubleshooting, environment configuration management, and SQL performance tuning (execution plans, query rewrites, indexing). Has also debugged cross-layer incidents involving DB locks and network latency, and modifies Python/XML automation scripts to meet customer-specific requirements while improving logging and performance.”

“LLM/agent builder who has shipped production AI systems in the wellness space, including an LLM-powered food tracking product used by 5000+ users and a voice/call-routing onboarding workflow using LangGraph/LangChain with LiveKit and Twilio. Strong focus on practical reliability work: latency reduction, retrieval/embedding tuning, and CI-driven evaluation with simulations and metrics.”

“Transformation-focused operator with experience at CVS Health and Wurth, leading usability analysis across dozens of programs during a major operational transformation. Known for data-driven prioritization and executive-ready communication (weekly metric/risk updates, concise progress briefs) to drive cross-functional alignment and measurable operational improvements.”

“Data scientist with hands-on experience building an XGBoost-based customer segmentation/churn risk scoring model used by sales and marketing teams. Emphasizes production-grade practices—efficient SQL for large-scale data pulls, rigorous data validation/testing, and scalable, modular Python code designed to support multiple customer types.”

“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”

“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”

“Built a Python-based geospatial machine learning backend for PFAS contamination risk mapping, including reproducible feature pipelines, ensemble modeling, and a FastAPI layer for visualization/analysis. Emphasizes data integrity and robustness (CRS/coverage checks, fail-fast validation) and has led safe backend refactors using feature flags, idempotent backfills, and Postgres RLS for secure, queryable results delivery.”

“Strategic sourcing/procurement professional with experience at Gumpro and Micron leading indirect (MRO/CapEx) and critical component sourcing initiatives. Uses structured category strategy (spend/Kraljic/Pareto), RFX, risk management, and TCO/should-cost models; reports $10M+ savings and lead-time reduction from 60 days to 2 days while mitigating geopolitical/tariff exposure and strengthening supply resilience.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Senior Unity UI/UX developer who led XR interaction design for a VR meetings/events platform (VRChat/Horizons-like), including a modular radial menu that served as the app’s primary control system. Also built a Pokemon Go-style Unity AR experience for a major shopping centre end-to-end, tackling real-world marker scanning challenges (lighting, reflections, installation) through iterative prototyping and testing.”

“Software engineer who owned an Order Management System end-to-end at Reliance Jio, improving large-table performance via UI virtualization shipped behind feature flags and refined through direct ops-user observation. Also built an OCR automation tool at Piramal Realty using Python/Tesseract with validation and manual correction fallbacks, driving adoption by operations teams. Experienced integrating with Kafka-based microservices and improving observability using structured logging and correlation IDs.”

“Full-stack engineer with strong data/regulatory reporting background (BNY) who owns customer-facing and internal reporting products end-to-end—from ETL/SQL transformations through React/TypeScript UIs and Spring Boot APIs. Built role-based, audit-friendly dashboards and designed RabbitMQ-based event-driven microservices with reliability patterns (idempotent consumers, publisher confirms, Saga) to scale workflows across teams.”

“Python backend engineer who built a personal LLM-powered AI code review tool that parses code into context-preserving diff chunks and uses the OpenAI API to analyze and summarize changes. Has hands-on Kubernetes deployment experience (replicas, rolling updates, ConfigMaps/Secrets, health probes) and follows GitOps-style, declarative CI/CD workflows; also has experience designing streaming/event-style processing with attention to reliability and observability.”

“Robotics engineer who led core autonomy stack development at Spacer Robotics (Isaac ROS/ROS2) spanning sensor integration, SLAM/mapping, navigation, and validation. In a research lab thesis, built three mobile robots from scratch and created a distributed multi-agent collaboration framework with blockchain-based incentive models, demonstrating depth in both hands-on robotics and distributed systems.”

“Darwinbox team member who supported talent/recruiting operations while also driving product improvements across HR modules (recruitment, onboarding, payroll, performance). Led a small team (5–6) and implemented discovery-driven configuration and BI reporting (Power BI/Tableau/Confluence), including a reported 30% reduction in recruitment configuration issues and real-time funnel reporting to support fast hiring.”

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

“Built and shipped production generative AI/RAG assistants in regulated financial contexts (S&P Global), automating compliance-oriented Q&A over earnings reports/filings with grounded answers and citations. Experienced across the full stack—AWS-based ingestion (PySpark/Glue), vector retrieval + LangChain agents, GPT-4/Claude model selection, and production reliability (monitoring, caching, retries) plus rigorous evaluation and regression testing.”

“Built and deployed production LLM assistants for internal Q&A and customer-feedback summarization, emphasizing reliability (RAG, prompt tuning, validation/whitelisting) and privacy safeguards. Improved adoption by adding explainable outputs and a user feedback mechanism, and has hands-on orchestration experience with Aflow and Azure Logic Apps.”