Vetted Data Cleaning Professionals

“Backend engineer with deep experience building event-driven logistics systems (orders, warehouse execution, real-time delivery tracking) using Spring Boot/PostgreSQL/Redis and strong observability (Prometheus/Grafana). Led a zero-downtime migration from monolithic MySQL to a sharded architecture for ~2M users with dual-write, checksum validation, and fast auto-rollback, and has strong security expertise including PostgreSQL RLS for multi-tenant SaaS and robust OAuth/JWT handling.”

“Full-stack engineer with strong Next.js App Router + TypeScript experience who built and owned a production internal analytics dashboard end-to-end, including server-component data fetching, route handlers for secure proxying, and post-launch monitoring/caching fixes. Also designed Postgres data models and performance-tuned analytics queries, and built reliable BullMQ/Redis-based order-fulfillment workflows with idempotency, retries, and compensating refunds—comfortable operating with high ownership in early-stage teams.”

“Built and operationalized intelligent pricing infrastructure for live event ticketing at StubHub, emphasizing iterative prototyping with traders and production-grade monitoring (Splunk, API/data-stream thresholding). Also partnered with customer-facing teams to drive adoption and helped win a significant consignment revenue-share deal by demoing the system to the Philadelphia 76ers and quantifying pricing efficacy and business impact.”

“ML engineer/data scientist with M&T Bank experience who built a production reinforcement-learning portfolio analytics tool for wealth management, emphasizing near real-time performance via batch/serving separation and robust generalization through stress-scenario backtesting and RL regularization. Strong MLOps background (Airflow, Grafana, MLflow) and proven ability to drive adoption with non-technical stakeholders using KPI alignment and SHAP-based explanations.”

“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”

“Founder/director of the music and event production company Tuned In, personally driving outbound BD to book underground LA talent and produce 25 events, including a major Lunar New Year collaboration with Alice Longyu Gao. Also ran multi-channel voter/community outreach for the Frankie Carrillo campaign (door knocking, 1,000+ SMS) and built automation tools (Apple widget; AI image-to-CSV workflow at Live Nation) to improve operational efficiency.”

“Org/people advisor with 20+ years applying systems thinking and data-driven methods to high-stakes change—spanning DEI transformations at an international consulting firm, profitability turnarounds in eldercare via company-wide facilitation, and executive coaching. Also designs compensation benchmarks for novel roles at international tech companies, enabling successful global hiring and growth.”

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“Built and shipped a real-time oncology risk prediction system used by doctors during patient visits, trained on clinical data in AWS SageMaker and deployed via FastAPI with sub-second responses. Emphasizes clinician-trust features (SHAP explainability, validation checks) and HIPAA-compliant controls (encryption, RBAC, audit logging), plus Kubernetes-based production operations with autoscaling, monitoring, and drift/retraining workflows; collaborated closely with oncologists at Flatiron Health.”

“ML/NLP practitioner with experience building an embedding-based ad matching and search system at Vericast (BERT embeddings + similarity search) to replace a third-party taxonomy approach, evaluated via a human-curated gold standard. Also built a custom NER pipeline at Allstate for auto accident claims calls using a bidirectional LSTM and achieved 90%+ F1, with a strong emphasis on production-grade ML workflows (testing, CI/CD, orchestration, versioning, validation).”

“Former PwC Director with hands-on early-stage venture experience (e.g., BridgeLights, a big-data analytics concept for early fintech) spanning concept creation, platform architecture, and go-to-market experimentation. Strong focus on building scalable, modular data platforms with rigorous governance/compliance (data lineage, quality controls) and supporting technical diligence in investor-aligned environments.”

“Worked on Sophia the humanoid robot, building production animation pipelines and enhancing human-robot interaction via perception and behavior orchestration. Experienced in stabilizing noisy perception-driven state transitions and designing smooth, user-centered behavioral flows, collaborating closely with artists, animators, and experience designers to translate creative intent into measurable system behavior.”