Vetted Data Visualization Professionals

“Built and productionized an LLM-enabled system visualization web app at Longshot, designed modularly to pivot quickly to a mobile-friendly interface as customer needs changed. Experienced in diagnosing LLM/agentic workflow failures using observability, deterministic replay, and fault-tree root cause analysis. Also delivers developer-focused demos and trainings (including robotics deployment/mapping at Meta) and partners with sales as a technical closer, including for government clients by demonstrating failure modes and system modularity.”

“Enterprise CSM with experience at IBM and Cloudera, focused on AI and cloud modernization engagements. Drives adoption, renewals, and expansion by building success plans with measurable ROI/usage outcomes, mediating complex stakeholder priorities, and unblocking deployments through tight Product/Engineering/Sales coordination. Also brings strong GTM analytics capability (Salesforce/HubSpot/GA/Tableau/SQL) to improve funnel performance and pipeline conversion.”

“Early-stage investing/venture sourcing profile with experience building a structured founder-sourcing pipeline across online communities, universities/hackathons, and in-person events. Interned at OrCap LLC, where they converted cold outreach into qualified founder conversations by staying useful over time (insights, relevant intros) and running structured first calls that led to internal greenlights for deeper diligence.”

“CRM and lifecycle marketing analyst with 4+ years of experience spanning Accenture client work and Walmart-focused CRM, loyalty, and eCommerce analytics. Strongest in segmentation, campaign measurement, and A/B testing, with reported impact including 14% higher response rates, 8% repeat order growth, 9% CTR lift, and 7% conversion improvement.”

“Recruiting/Talent Operations leader with deep Lever ATS and analytics experience who led a high-impact Grace Hopper Conference program, creating attribution tracking via unique links and coordinating IT/engineering/TA to drive thousands of applicants and improved operational outcomes. Redesigned recruiter–hiring manager intake and alignment workflows with automation, improving time-to-fill by ~20%, and led compliance initiatives including pay transparency and ADA-accessible job descriptions using CommonLook/PAC in partnership with Legal and Compensation.”

“AI engineer building and deploying deep-learning-based optimization/control systems for petrochemical plants, with a focus on maintaining operational stability under real-world constraints. Core contributor to model and inference design; introduced a stability-focused non-linear objective and sped up second-layer optimization via on-the-fly first-order approximations. Experienced using Kubernetes for end-to-end testing and effective in translating customer expectations into measurable evaluation plots for non-technical stakeholders.”

“Built a production AI agent for phishing fraud detection using n8n orchestration, Claude (Sonnet 4/MCP), VirusTotal, and JavaScript formatting to generate and deliver email-based reports via Gmail. Has experience evaluating detection accuracy against known examples, iterating via feedback, and presenting AI solutions to non-technical teams.”

Syed Daim Ali

Screened

Intern Software Engineer specializing in FinTech and AI platforms

Sunnyvale, CA0y exp

“Systems-focused engineer who built an OS kernel with multithreading, priority scheduling, system calls, and synchronization primitives, and debugged race conditions end-to-end. While not yet hands-on with ROS/SLAM, they clearly connect low-level concurrency and scheduling decisions to deterministic, reliable robotics-style real-time workloads.”

“Full-stack/product-minded engineer with recent experience in both an early-stage AI startup and a B2B payments marketplace. Stands out for building a pgvector-based semantic cache that reduced LLM latency by 35% and for shipping audit-heavy payment infrastructure with Stripe/Plaid, idempotent webhook handling, and major reconciliation query optimizations.”

“Robotics software engineer who built the full control stack for a fleet of manufacturing/repair robots in Relativity Space R&D (perception, planning, motion control, integration, deployment). Has ROS/ROS 2 experience spanning custom SLAM (LiDAR+IMU), multi-robot coordination, and multi-drone control (Pixhawk 4, minimum-snap trajectories), with strong real-world debugging and simulation/CI testing practices (Gazebo, CI/CD, some Docker).”

Priyanshu Maurya

Screened

Mid-level Data Scientist specializing in insurance, finance, and healthcare analytics

New York, NY3y exp

“Built and productionized LLM-driven sentiment scoring for earnings call transcripts at Goldman Sachs, replacing legacy NLP to deliver a cleaner trading signal while managing latency/cost via batching, caching, and distilled models. Also implemented an Airflow-orchestrated fraud modeling pipeline at MetLife with drift-based retraining and SageMaker deployment, and has a disciplined evaluation/rollout framework for reliable AI workflows.”

Tianxiang Yang

Screened

Mid-level Full-Stack Engineer specializing in MERN and FinTech

Arkansas, AR4y exp

“Frontend/full-stack engineer with Walmart experience building browser-based user features end to end, including an email mention notification system and performance improvements on e-commerce tooling. Strongest themes are React-based UI development, optimization, and refactoring legacy codebases toward more maintainable functional-component architecture.”

“Data Analyst at U.S. Cellular who built production LLM solutions, including a Tableau-embedded chatbot that converts natural language questions into Oracle SQL and returns actionable KPI insights for non-technical users. Also authored MAD-CTI, a multi-agent LLM system for dark web hacker forum threat intelligence (published in IEEE Access) that outperformed single-agent approaches by 14%.”

“Analytics professional with hands-on experience building SQL and Python workflows for support operations and product reporting. They stand out for turning messy CRM, ticket, and activity data into validated, performance-optimized reporting tables and dashboards, while partnering closely with stakeholders to standardize KPI definitions around SLA performance and retention.”

“Backend/AI engineer who has built an offline, citation-grounded RAG system end-to-end with hybrid retrieval, local LLM inference, and quantitative evaluation via RAGAS. Also brings real-time systems experience from an Airbnb-like booking platform and data pipeline/ML quality work from a Bilibili internship, with a strong emphasis on reliability, privacy, and measurable correctness.”