Vetted Data Cleaning Professionals

“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.”

Benjamin Hudnut

Screened

Junior Live Events & Music Operations professional specializing in booking, touring, and community events

Los Angeles, CA4y exp

“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.”

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.”

“Analytics-focused candidate with hands-on experience building SQL data pipelines and Python-based forecasting workflows for inventory and planning use cases. They emphasize data quality, stakeholder trust, and operational adoption, citing a 19% forecast accuracy improvement and strong experience translating analytics into dashboard-ready business metrics.”

Bhaskar Reddy

Screened

Junior Business & Data Analyst specializing in analytics and BI

Fairfield, CT2y exp

“Analytics-focused candidate with hands-on experience building SQL and Python workflows that turn messy multi-source data into reporting assets and dashboards. They show strong practical judgment around data quality, table grain, validation, and performance tuning, and they described an education-focused engagement project that reportedly improved course completion by 15% through targeted interventions and metric-driven stakeholder alignment.”

“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”

“CRM/lifecycle marketer with hands-on ownership of high-volume, multi-channel programs across email, SMS, and push, including Braze journey design, QA, deployment, and post-campaign analysis. Stands out for combining strong campaign operations with incrementality measurement and experimentation, including a 20% conversion improvement from journey optimization and 12% incremental revenue lift identified through holdout-based analysis.”

“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.”

Vidhi Chhatbar

Screened

Mid-level Full-Stack Engineer specializing in cloud microservices and FinTech

Los Angeles, CA5y exp

“Software engineer with experience across enterprise (AIG, MSCI) and an early-stage startup (Job Map), owning production systems end-to-end. Built secure insurance microservices on Spring Boot with JWT/RBAC and AWS-based CI/CD/observability, plus Kafka streaming pipelines for financial data. Also shipped a GenAI personalization MVP using FastAPI and LLM APIs in a high-ambiguity startup environment.”

“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.”

“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.”

“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”

“Procurement/sourcing professional with deep end-to-end ownership of recurring program supply and services sourcing (materials plus logistics/fulfillment), focused on reducing unit and landed costs while improving OTIF and tightening SAP purchasing controls. Demonstrates structured supplier vetting (capability, capacity, execution reliability), strong negotiation on commercial terms and SLAs, and proactive mitigation of international trade/freight/duty volatility through risk-adjusted sourcing strategies.”

“Built and productionized an NLP-powered customer support assistant at JPMorgan Chase for digital banking, focused on reducing response time for repetitive client queries. Strong in real-world AI deployment challenges—sensitive data handling, low-latency FastAPI services, and AWS/Kubernetes operations with CI/CD—plus a metrics- and guardrails-driven approach to reliable AI workflows.”

“Built and deployed a secure, production LLM-based document summarization and risk-highlighting tool for financial auditors, running inside a private Azure environment to protect confidential data. Focused on reliability (hallucination mitigation via retrieval-based prompts and source citations) and validated performance through comparisons to auditor summaries plus a user pilot, cutting review time by about half.”

“ServiceNow engineer who built and launched a production LLM-powered ticket resolution/knowledge assistant using RAG (LangChain + Hugging Face embeddings + vector search) integrated into internal support dashboards via REST APIs. Optimized the system from ~6–8s to ~2–3s latency while improving usability with concise, cited answers and guardrails (grounding + similarity thresholds), delivering ~30–35% reduction in manual ticket investigation effort.”

“Uber engineer who has owned internal products end-to-end across backend (Spring Boot microservices, MySQL) and frontend (React), including performance optimization and secure JWT-based auth. Also shipped a production internal RAG/embeddings LLM support assistant over policy docs and support tickets, with guardrails (confidence thresholds, human review) and an evaluation loop that directly reduced hallucinations.”

“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.”

Nirmal Attarde

Screened

Mid-Level Software Development Engineer specializing in backend microservices and cloud

3y exp

“Software engineer with Oracle experience deploying a BioCatch fraud-detection integration into HDFC Bank’s core banking platform, using phased rollout and real-time monitoring and reporting ~80% fraud reduction. Also built a modular speech-to-text product (VocalSense AI) achieving ~95% accuracy and has strong production incident response skills (15-minute recovery) plus AWS serverless API hardening for messy inputs.”

“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.”

“Analytics-focused candidate with hands-on experience using SQL, Python, and Power BI to turn fragmented event and user data into reliable reporting layers, automated workflows, and stakeholder-facing dashboards. They appear strongest in product/growth analytics, especially funnel, conversion, retention, and user behavior analysis, with clear examples of driving product improvements through metric design, segmentation, and ongoing monitoring.”

“ML/AI engineer with hands-on experience shipping regulated financial AI systems at JPMC and Capgemini, spanning credit risk, fraud detection, and generative AI assistants. Stands out for combining modern LLM/RAG architectures with strong MLOps, real-time infrastructure, and explainability/compliance practices, while delivering measurable business impact in latency, accuracy, cost, and risk reduction.”

“Growth marketer from earthnutre who led a zero-to-one conversion and traffic-quality improvement project combining SEO, landing page/CRO, and paid-media measurement. Used SEMrush-driven high-intent keyword restructuring plus phased page optimizations with control comparisons, then tied results to paid performance—reporting ~15% lower CPA and ~30% MoM ROAS lift. Also runs a modular, data-driven creative iteration process across Meta/TikTok/YouTube and provides performance-based guidance to creators/editors.”