Vetted Data Quality Professionals

“Operator/program leader with experience building operating models and execution cadences for data/analytics and energy data services across Entech, HealthEquity, and RealPage. Has launched an undefined energy data management service into regulated enterprise environments and driven cross-functional alignment via dashboards, SLAs, and OKRs—contributing to enterprise wins and a successful company sale.”

“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.”

“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.”

“Built and shipped LLM/OCR/NLP-driven document-intelligence workflows in operational environments (EnvoyX and UPS), emphasizing production readiness via explicit state-machine orchestration, confidence gates, and human-in-the-loop review. Demonstrated strong business impact in customs brokerage/document ingestion: 50% fewer customs rejects, 30% higher throughput, SLA adherence improved from 71% to 96%, and platform reliability reaching 99.6% with 78% fewer bad-data incidents.”

Shrey Kumar

Screened

Junior Full-Stack Software Developer specializing in web platforms and AI workflows

Dallas, TX1y exp

“Software engineer who built and shipped “Counsellor AI,” a production LLM-powered academic advising agent for college students using AWS Bedrock and grounded RAG over official university catalogs/policies. Emphasizes reliability through structured JSON outputs, multi-step orchestration with shared state, and strict intake/validation gates to prevent hallucinations and invalid academic plans; also has experience hardening messy telecom operational data pipelines with normalization, permissions, fallbacks, and idempotent patterns.”

Richard Bemis

Screened

Executive CIO/CTO/CDO specializing in data, AI/ML, and digital transformation

Long Beach, CA21y exp

“Founder building a healthcare provider data management startup who has progressed from problem identification to product architecture, patent filing, prototype development, beta customer outreach, and angel fundraising. They also have experience performing technical assessments for VCs and approach company-building with a structured focus on customer demand, risk mitigation, IP protection, and candid core-team formation.”

“Finance data and reporting professional with PwC experience who bridges accounting and technology, especially around GL-related reconciliations, reporting accuracy, and close support. While not a direct PeopleSoft GL owner, they bring strong SQL-driven troubleshooting, ETL/data mapping remediation, and process automation experience that helped shorten close cycles and improve audit readiness.”

“Analytics candidate with hands-on experience turning messy pricing and competitor data into reporting-ready SQL tables, plus building Python automation workflows that replaced manual processing across 40,000 images at roughly 89% accuracy. They also led a price elasticity analysis that informed differentiated pricing strategies and supported reporting through Power BI dashboards.”

“Analytics professional with Deloitte experience building SQL and Python workflows for revenue, pipeline, and opportunity analytics at scale. They combine strong data engineering and modeling skills with business-facing delivery, citing impacts including 8-10% conversion improvement, ~$700K revenue protected, 12% YoY project acquisition growth, and 15% retention improvement in financial services.”

Shiven Arya

Screened

Junior data and product analyst specializing in machine learning and analytics

Ann Arbor, MI2y exp

“Senior at the University of Michigan who led most of the technical build for a real client-facing Medicare fraud detection system with explainable ML and an analyst-ready Streamlit dashboard. Also builds practical LLM tools independently, including a market sentiment pipeline over Reddit/news data and a resume parser/grader, showing strong product instinct alongside applied ML and data engineering depth.”

“Data/automation-focused engineer with hands-on experience building production workflows across marketing, sales, and RevOps at ZoomInfo. They’ve owned end-to-end automations spanning Snowflake/Databricks pipelines, ad platform API integrations, LLM-powered sales prep and deal summarization, and ML-based account prioritization.”

Aakash Khepar

Screened

Mid-level Full-Stack AI Engineer specializing in agentic AI systems

Tempe, AZ4y exp

“AI/full-stack builder with hands-on experience shipping healthcare, career-tech, nonprofit, and fintech products, spanning speech AI, browser extensions, agentic RAG systems, and enterprise ML monitoring. Stands out for combining strong technical depth with measurable outcomes, including reducing clinical call WER from 26% to 3%, building safe tool-using agents with rollback/RBAC, and delivering zero-to-one multi-tenant platform features in ambiguous environments.”

“Robotics researcher/team lead from ASU’s Robotics and Intelligent Systems Lab who rebuilt a failing soft-robotics test bench into a modular 8-Arduino, ROS 2/Python-controlled data-collection system, doubling sampling performance and dramatically reducing downtime. Currently doing thesis work on physics-informed neural network (PIRNN/RNN) modeling of a pneumatically actuated soft robotic arm using experimentally collected trajectory/pressure data.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“Built and shipped a production LLM-powered healthcare support agent at UnitedHealthGroup, using LangChain + FAISS RAG on AWS SageMaker with CloudWatch monitoring and human-in-the-loop fallbacks for safety. Strong focus on reliability engineering (confidence gating, retries/timeouts, caching) and continuous evaluation loops; reported ~40% improvement in query resolution efficiency while reducing manual support workload.”

“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”

Samatha Amsala

Screened

Mid-level Data Engineer specializing in cloud data warehousing and analytics

Omaha, NE6y exp

“Data engineer at American Express who owned end-to-end pipelines for transaction and customer data used in finance reporting and risk analytics, processing ~5–8M records/day. Built Airflow-orchestrated ingestion (including external APIs/web sources) with strong data quality controls, monitoring/alerts, and resilient backfill/retry patterns, and also shipped a versioned REST API serving aggregated metrics to analytics teams.”

John Hoffman

Screened

Senior Data Engineer specializing in Databricks, Spark, and AWS for government healthcare data systems

Windsor Mill, MD12y exp

“Python/AWS engineer focused on batch-processing and data workflows, including building reusable S3/boto3 utilities with reliability features and IAM-based auth. Has led low-risk legacy modernizations using parity testing plus a month of parallel production runs, and has owned production issues end-to-end (including fixing a client-side Excel macro) while contributing to significant AWS cost reductions (~$10k/month).”

“Analytics professional with experience spanning HSBC and the University of Buffalo, combining banking risk/portfolio analytics with reproducible Python and SQL reporting workflows. Stands out for cleaning complex multi-source data, standardizing business metrics across dashboards, and delivering measurable impact including an 80% reduction in manual reporting and an estimated GBP 1.2M profit contribution from scorecard redevelopment.”

Chaitanya Prasad Reddy Narala

Screened

Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems

USA4y exp

“Senior AI/ML engineer focused on production LLM systems, combining RAG, fine-tuning, distributed training, and AI safety to ship scalable real-time moderation and conversational AI platforms. Stands out for pairing deep AWS/Kubernetes MLOps expertise with measurable impact: 40% lower latency/cost, 30-50% fewer hallucinations, and major reliability gains through observability and automation.”